Virtual Try-On (VTON) promises to transform the apparel e-commerce industry, offering benefits for shoppers, businesses, and the environment. This workshop will address three key challenges that must be overcome to realize VTON's full potential: achieving high-fidelity, rapid video try-ons; accurately predicting 3D garment size and improving 3D human body reasoning; and defining robust metrics for synthesis quality that avoid offensive results across diverse demographics. Addressing these VTON-specific challenges will necessitate fundamental advancements in generative image and video synthesis, offering broader impact within the computer vision and machine learning communities.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The 1st Workshop on Distillation of Foundation Models for Autonomous Driving (WDFM-AD) aims to advance the deployment of large foundation models—such as vision-language models (VLMs) and generative AI (GenAI) models—within autonomous driving systems through efficient distillation techniques. Building on the momentum of prior workshops focused on large language and vision models for autonomous driving, WDFM-AD provides a dedicated platform for researchers and industry practitioners to explore methods that bridge cutting-edge foundation model research with real-world deployment, particularly under the stringent latency and resource constraints of autonomous vehicles. By addressing the challenges of compressing, aligning, and deploying foundation models for self-driving, WDFM-AD seeks to accelerate their safe, efficient, and scalable integration into next-generation autonomous driving systems.

World models are predictive systems that enable Physical AI agents to understand, decide, plan, and analyze counterfactuals through integrated perception, instruction processing, controllability, physical plausibility, and future prediction capabilities. The past year has witnessed significant advancements from both academic and industrial research teams, with various models utilizing different conditioning approaches (text, image, video, control) being released openly and commercially. While these developments enable applications in content creation, autonomous driving, and robotics, the models' diversity in training methods, data sources, architecture, and input processing necessitates critical evaluation. The WorldModelBench workshop aims to address this need by fostering discussions on evaluation criteria (physical correctness, prompt alignment, generalizability), metrics development, standardized methodologies, and crucial topics including accessible benchmarking, quantitative evaluation protocols, downstream task assessment, and safety/bias considerations in world models.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The Event-based Vision Workshop at CVPR is the premier venue for discussing exciting new ideas about neuromorphic cameras and their processing methods. It covers the sensing hardware, as well as the processing, data, and learning methods needed to take advantage of event-based cameras. The workshop aims to highlight an emerging field with the potential to overcome many of the limitations of frame-based systems (speed, power consumption, robustness to HDR illumination, etc.). This forum fosters community building around these novel cameras, capitalizing on a growing interest and increasing contributions at the main conference. Furthermore, the workshop seeks to connect with a broader audience by highlighting interdisciplinary links between computer vision, robotics, artificial intelligence, computational neuroscience, and psychology, as event cameras facilitate research into replicating the efficiency and robustness of the human visual system.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

This workshop will feature keynotes and presentations at the cutting-edge of foundation models and large vision models for remote sensing. It will bring together researchers working on both foundation and large vision models and geospatial image analysis to address the nuances presented by using such emergent models for remotely sensed imagery (e.g. a multitude of sensors with different sensing characteristics/specifications, diverse imaging modalities, ranging from passive-optical multi/hyperspectral to active-imaging such as SAR and LiDAR; limited ground-reference data etc.). Our emphasis will range from large vision and foundation models that are showing promise in the computer vision community to foundation models that are pre-trained on large-quantities of earth-observation imagery. This workshop will provide a venue for the community to present works that push the envelope on adapting these models for effective inference of multi-sensor, multi-temporal, multi-scale earth observation imagery.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

This workshop focuses on the core principles of efficiency in large-scale vision models. How do we minimize redundant operations in generative models without compromising quality? Can autoregressive decoding and diffusion sampling be accelerated through parallelization? What are the trade-offs between compression, quantization, and expressivity? We seek to advance new directions in compact model representations, adaptive computation, parallel decoding, and structured sparsity—approaches that go beyond incremental optimizations and redefine how LVMs operate.

We invite researchers working on fast and scalable vision architectures, low-cost inference, and efficient generative models to share their insights. Whether through sampling acceleration, efficient transformers, new architectural paradigms, or theoretical limits of model compression, this workshop provides a platform to discuss how LVMs can be optimized for both performance and practicality.

Join us in shaping the next generation of vision models—where efficiency is not just a constraint, but a driving force for innovation.

This workshop addresses a critical gap in current AI research by focusing on the integration of language and 3D perception, which is essential for developing embodied agents and robots, especially considering the recent rise of multimodal LLMs and vision-language-action (VLA) models.

The workshop will explore challenges and opportunities in this area, providing a platform for researchers to share their work, discuss future directions, and foster collaboration across disciplines including robotics, computer vision, natural language processing, and human-computer interaction.

Visual concept discovery aims to extract compact and structured representations of the visual world, and recompose them to tackle novel intricate problems. It has played a crucial role in many core problems in computer vision research, including both discriminative and generative tasks. An important research question is to understand and design concept representations that facilitate better learning from various datasets and compositional reasoning. As an endeavor to answering this question, in this workshop, we gather together researchers in computer vision, multi-modal learning, machine learning, and cognitive science to discuss the development and interpretation of visual concept learning systems and their applications.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

In recent years, foundation models have gained significant traction and success. Such foundation models were shown to effectively adapt across various downstream tasks, with strong generalization capabilities, especially in zero-shot and few-shot scenarios. There is growing interest and progress specifically in vision foundation models (VFM). Some of the latest models include those trained using self supervision, such as the DINO series, and those utilizing image/text like CLIP, Flamingo, Llava and Cambrian. Various pixel-level vision foundation models have also emerged for image/video referring segmentation and depth estimation. Our workshop aims to bring together researchers dedicated to developing and adapting vision foundation models for pixel-level understanding tasks, including image/video segmentation, referring image/video segmentation and reasoning, tracking, depth estimation, and motion estimation. We will explore major directions in pixel-level understanding with vision foundation models and discuss the opportunities they present, particularly in low-resource settings that could have a positive societal impact. Additionally, we will discuss the risks associated with these models and explore methods to mitigate them. The workshop features seven invited talks, mixing emerging and established researchers, along with posters and selective spotlight presentations.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Despite the growing momentum around 3D reconstruction and generative AI in computer vision, a critical gap remains: how to create photorealistic, fully functional 3D digital twins that are indistinguishable from their real-world counterparts and enable practical applications. This workshop tackles that challenge by spotlighting 3D digital twin creation technologies and their broad impact across AR/VR, spatial and contextual AI, and robotics. Distinguished speakers from diverse disciplines will share cutting-edge digital twin creation techniques and real-world use cases. Additionally, we are excited to launch a benchmark and challenge for 3D digital twin creation, built on our Digital Twin Catalog (DTC) dataset and supported by open-source baselines. This initiative aims to spark meaningful discussion, foster collaboration, and accelerate progress in both academic research and practical deployment.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Anomaly detection—also known as novelty or out-of-distribution detection—is a key challenge in computer vision and pattern recognition. From medical imaging to industrial inspection, spotting what doesn’t belong is critical, yet notoriously hard. Why? Because anomalies can take unlimited forms, and most models see only a narrow slice of the possible "normal" during training.
The VAND workshop brings together cutting-edge research tackling this open-set problem across supervised, semi-supervised, and unsupervised methods, as well as few-, one-, and zero-shot approaches.
This year, we're also hosting two exciting challenges: (1) 'Adapt & Detect – Robust anomaly detection in real-world applications', and (2) 'VLM Anomaly Challenge – Few-shot learning for logical and structural anomaly detection using vision-language models'.
Join us to explore the next generation of models that can detect the unexpected.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

This workshop explores the evolving intersection of computer vision and physics, where two competing perspectives—physics-based simulations versus data-driven approaches like video foundation models—seek to model the world effectively. By bringing together researchers from both fields, the event aims to foster collaboration, identify synergies, and advance applications in scientific research, generative AI, robotics, gaming, and extended realities (XR). Through presentations and discussions, the workshop will promote interdisciplinary dialogue to develop next-generation technologies that combine physics-based and data-driven methods, ultimately enhancing realistic simulations for immersive environments, automated tasks, and seamless virtual-physical integration.

Sign languages are visual languages and a key form of communication for deaf communities. Thanks to recent advances in deep learning and computer vision and the availability of larger datasets, significant progress has been made in sign language technologies. Following the first and second editions, this workshop is motivated by the desire to broaden participation in sign language research from the computer vision community. It aims to bring together researchers working on different aspects of vision-based sign language research and sign language linguists to explore recent advances and future directions in sign language recognition, translation, and production.

Please visit our schedule page for details: https://slrtpworkshop.github.io/schedule/

Egocentric devices like wearable cameras, smart glasses, and AR/VR headsets are rapidly evolving to automatically recognize user actions, environments, gestures, and social interactions. This workshop serves as a central gathering point for the egocentric vision community to exchange ideas and explore this fast-growing field. It features challenges across five major datasets (EPIC-Kitchens, Ego4D, Ego-Exo4D, HoloAssist, HD-EPIC), keynote talks from leading experts, abstract presentations on emerging ideas, EgoVis award to seminal papers from 2023/2024, and poster sessions on pivotal papers—offering a comprehensive look at the future of egocentric perception and wearable AI.

This workshop aims to explore the pathway toward building “Embodied Humans”—intelligent humanoid agents capable of both physical action and cognitive reasoning like humans—where the boundary between digital avatars and physical humanoid robots could be dissolved through their co-evolution across virtual and real worlds. We will examine this synergy‘s possibility through three core dimensions: 1) how humanoid robots learning foundational “genes” from avatars? 2) how virtual humans gain physical plausibility from robots‘ embodiment to enrich realism and interactivity? and 3) how both systems develop self-autonomy to perceive, plan, and act in dynamic, open-ended environments? Featuring academic researchers and industry experts as invited speakers and panelists, the workshop brings together perspectives from virtual avatar modeling and humanoid robot learning to explore how systems on both ends are progressing toward human-like capacities for perception, reasoning, and movement. Through advanced techniques—such as reinforcement learning, cognition modeling, motion and structure perception, geometric representations, multimodal simulation, and large language/vision/action models—we aim to understand how virtual humans are evolving beyond surface-level realism, and how humanoid robots are advancing beyond pre-scripted skills—enabling both to engage the world with situational understanding, behavioral adaptability, and autonomous intent. At the heart of this workshop lie two essential questions: What makes a virtual human real—not just to see, but to know? And what does it take for a humanoid robot to not just move, but to become?

Recent advances in generative modeling and semantic understanding have spurred significant interest in synthesis and understanding of 3D scenes. In 3D, there is significant potential in application areas, for instance augmented and virtual reality, computational photography, interior design, and autonomous mobile robots all require a deep understanding of 3D scene spaces. The ScanNet++ workshop offers the first benchmark challenge for novel view synthesis in large-scale 3D scenes, along with high-fidelity, large-vocabulary 3D semantic scene understanding -- where very complete, high-fidelity ground truth scene data is available. This is enabled through the new ScanNet++ dataset, which offers 1mm resolution laser scan geometry, high-quality DSLR image capture, and dense semantic annotations over 1000 class categories. In particular, existing view synthesis leverages data captured from a single continuous trajectory, where evaluation of novel views outside of the original trajectory capture is impossible. In contrast, our novel view synthesis challenge leverages test images captured intentionally outside of the train image trajectory, allowing for comprehensive evaluation of methods to test new, challenging scenarios for state-of-the-art methods.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The CVPR community has long focused on evaluating AI systems for their general scene-understanding capabilities. However, as these models are deployed globally, it is essential that they also understand cultural concepts and values, ensuring they cater to the diverse needs of users. This workshop expands computer vision frontiers by bringing together researchers from computer vision, natural language processing, AI ethics, and cultural anthropology to discuss how we can build geo-diverse and culturally aware vision-language models (or AI models in general). Specifically, the workshop will focus on evaluating the types of tasks, benchmarks, and metrics we should develop to advance AI systems' capabilities in this area and explore promising approaches to overcome the challenges. Second, the workshop will benchmark progress in geo-diverse and cultural understanding of vision-language models through the CulturalVQA and GlobalRG challenges, which will test critical abilities such as visual question answering and grounding in culturally diverse scenarios. The insights from this workshop extend beyond computer vision, with significant implications for fields like healthcare, education, and e-commerce, where culturally aligned AI can enhance user experiences. Additionally, the workshop aims to inspire further research in AI ethics, fairness, and responsible AI deployment.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

In recent years, diffusion models have rapidly overtaken previous methods to become the dominant approach in visual generative modeling, with widespread applications in generating images, videos, 3D objects, and more. However, these models also come with notable limitations, such as slow generation speeds, limited human intervention during the generation process, and challenges in modeling complex distributions like long videos.

This year, our Visual Generative Modeling workshop at CVPR aims to explore what lies beyond diffusion models in visual generative modeling. We will discuss novel insights, alternative approaches, and new possibilities in modeling and generating visual data. Join us for a full-day event featuring keynote talks from both academia and industry -- all designed to ignite innovative ideas and novel research in visual generative modeling.

The Sixth Annual Embodied AI Workshop brings together researchers from computer vision, language, graphics and robotics to share the latest advances in embodied intelligent agents that see, talk, listen, reason, and act in bodies within interactive environments. This year's workshop focuses on Real World Applications, with topics including Embodied AI Solutions, Advances in Simulation, Generative Methods, and Foundation Models. The workshop will feature invited talks, a poster session, and panel discussions. Also, the sixth iteration of the workshop continues its tradition of highlighting several embodied AI challenges that advance the state of the art in the field.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The shift towards foundation models has overshadowed the unique insights of deep learning theory, resulting in a loss of valuable knowledge and resources for the community. As machine learning and computer vision extend into new domains, such as biology, a deeper understanding of vision tasks becomes increasingly important. This workshop will provide a crucial platform for discussing the systematic challenges of integrating theory and practice. Concretely, to bridge the gap between theoretical research in machine learning and its practical applications, the workshop aims to explore how theoretical tools can be leveraged to perform rigorous worst-case analysis, crucial for deploying machine learning models in safety-critical societal domains like healthcare, education, and sustainability.

AI for content creation plays a crucial role in domains such as photography, videography, virtual reality, gaming, art, design, fashion, and advertising, and lies at the intersection of computer vision, machine learning, computer graphics, and design. This workshop will provide attendees with a slice of cutting-edge techniques within this rapidly evolving field, considering both the fundamental technologies and practical challenges faced by designers and content creators, and will show successful applications of AI and deep learning in content creation. With invited speakers of world-class expertise in content creation, up-and-coming researchers, and posters from authors of submitted workshop papers, the workshop will help all to engage in a day filled with learning, discussion, and network building.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Many biological organisms have evolved to exhibit diverse behaviors, and understanding these behaviors is a fundamental goal of multiple disciplines including neuroscience, biology, animal husbandry, ecology, and animal conservation. These analyses require objective, repeatable, and scalable measurements of animal behaviors that are not possible with existing methodologies that leverage manual encoding from animal experts and specialists. Recently, computer vision has been making a significant impact across multiple disciplines by providing new tools for the detection, tracking, and analysis of animal behavior. This workshop brings together experts across fields to stimulate this new field of computer-vision-based animal behavioral understanding.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The workshop aims to bridge the gap between computer vision, artificial intelligence, and the life sciences, with a focus on transformative advancements in drug discovery. By integrating innovative imaging modalities—such as Spatial Transcriptomics, Cell Painting, and Optical Pooled Screening—with state-of-the-art computer vision techniques, this workshop seeks to foster collaboration between experts in biomedical science, AI, and computer vision.x000D
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The workshop highlights the potential for revolutionizing drug discovery processes, driving faster and more accurate identification of therapeutic targets, and expediting the development of treatments for complex diseases. Addressing pressing challenges like cancer, neurodegenerative disorders, and pandemics, the focus lies on leveraging AI to analyze high-dimensional biological data, enhancing our understanding of disease mechanisms and responses to therapies.x000D
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For the CVPR community, this represents an exciting opportunity to expand beyond traditional image processing tasks into applications with tangible societal impact. By applying computer vision expertise to critical healthcare and pharmaceutical challenges, participants will engage with tasks like multi-modal data fusion, enhancing explainability in biomedical applications, and addressing the unique complexities of biological imaging, such as sparse or noisy datasets.x000D
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This workshop is aligned with CVPR’s growing emphasis on “AI for social good,” offering computer vision researchers a platform to contribute to advances in medical science that could improve the lives of millions. It is a call to action for interdisciplinary innovation, uniting diverse expertise to tackle some of the most critical challenges in global health.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Vision-based detection and recognition studies have been recently achieving highly accurate performance and were able to bridge the gap between research and real-world applications. Beyond these well-explored detection and recognition capabilities of modern algorithms, vision-based forecasting will likely be one of the next big research topics in the field of computer vision. Vision-based prediction is one of the critical capabilities of humans, and the potential success of automatic vision-based forecasting will empower and unlock human-like capabilities in machines and robots.

One example application is in autonomous driving technologies, where vision-based understanding of a traffic scene and prediction of movement of traffic actors is a critical piece of the autonomous puzzle. Another area where vision-based prediction is used is the medical domain, allowing deep understanding and prediction of future medical conditions of patients. However, despite its potential and relevance for real-world applications, visual forecasting or precognition has not been the focus of new theoretical studies and practical applications as much as detection and recognition problems.

Through the organization of this workshop, we aim to facilitate further discussion and interest within the research community regarding this nascent topic. This workshop will discuss recent approaches and research trends not only in anticipating human behavior from videos but also precognition in multiple other visual applications, such as medical imaging, healthcare, human face aging prediction, early event prediction, autonomous driving forecasting, etc.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

This workshop addresses the gap between cutting-edge computer vision research and its practical application in industrial robotics, specifically addressing challenges in tasks like reliable, scalable, and cost-effective bin picking. The workshop brings together researchers and practitioners to discuss topics including 3D scene understanding, embodied AI, and robot learning, focusing on developing robust solutions by considering factors like embodiment, camera choice, and data needs. Complementing the workshop, the Perception Challenge for Bin Picking offers a practical platform for participants to tackle real-world 6DoF pose estimation problems using a robot-in-the-loop evaluation, providing a more realistic performance assessment than traditional vision-only metrics. The workshop and challenge together aim to accelerate the adoption of vision-guided robotics and enhance industrial automation efficiency.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Sports is said to be the social glue of society. It allows people to interact irrespective of their social status, age etc. With the rise of the mass media, a significant quantity of resources has been channeled into sports in order to improve understanding, performance, and presentation. For example, areas like performance assessment, which were previously mainly of interest to coaches and sports scientists are now finding applications in broadcast and other media, driven by the increasing use of on-line sports viewing which provides a way of making all sorts of performance statistics available to viewers. Computer vision has recently started to play an important role in sports as seen in for example football where computer vision-based graphics in real-time enhances different aspects of the game. Computer vision algorithms have a huge potential in many aspects of sports ranging from automatic annotation of broadcast footage, through to better understanding of sport injuries, coaching, and enhanced viewing. So far, the use of computer vision in sports has been scattered between different disciplines. The ambition of this workshop is to bring together practitioners and researchers from different disciplines to share ideas and methods on current and future use of computer vision in sports.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

The ABAW Workshop is a premier platform highlighting the latest advancements in multimodal analysis, generation, modeling, and understanding of human affect and behavior in real-world, unconstrained environments. It emphasizes cutting-edge systems that integrate facial expressions, body movements, gestures, natural language, voice and speech to enable impactful research and practical applications. The workshop fosters interdisciplinary collaboration across fields such as computer vision, AI, human machine interaction, psychology, robotics, ethics & healthcare. The workshop further addresses complex challenges like algorithmic fairness, demographic bias & data privacy, making it a vital forum for building equitable, generalizable & human-centered AI systems. By uniting experts from academia, industry & government, the workshop promotes innovation, drives knowledge exchange, and inspires new directions in affective computing, behavior modelling and understanding & human-computer interaction. Finally, the Workshop includes a Competition with 6 challenges, including valence-arousal estimation, basic & compound expression recognition, action unit detection, emotional mimicry intensity estimation and ambivalence/hesitancy recognition.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

With the recent success of computer vision and deep learning in various applications, there has been significantly increasing attention towards its use in agriculture, presenting both significant economic and social opportunities. This The 6th Annual International Workshop And Prize Challenge on Agriculture-Vision aims to foster research and applications at the intersection of computer vision and agriculture, addressing challenges in real-world agricultural scenarios, with a strong record from prior editions at CVPR 2020-2024. The workshop will feature a computer vision challenge, and invited speakers from diverse academic and industry backgrounds including computer vision, robotics, agriculture, and top industry practitioners. This event provides a platform to showcase current progress in interdisciplinary areas and encourage further research and development of Foundation Models In Agriculture.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

With the increasing adoption of machine learning models in high-stakes applications, rigorous audits of model behavior have assumed paramount importance. However, traditional auditing methods fall short of being truly experimental, as they rely on wild-caught observational data that has been manually labeled. Enter generative techniques, which have recently shown impressive capabilities in automatically generating and labeling high-quality synthetic data at scale. Critically, many such methods allow for the isolation and manipulation of specific attributes of interest, paving the path towards robust experimental analysis. x000D
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This workshop is dedicated to exploring techniques for auditing the behavior of machine learning models – including (but not limited) to performance, bias, and failure modes – by the controlled synthesis (via generation or simulation) of data. Of special interest are algorithms for generating data (images, text, audio, etc.) and benchmarking that provide reliable insights into model behavior by minimizing the impact of potential confounders. We also welcome work on the broader topic of using synthetic or quasi-synthetic data for model debugging, broadly construed, with the goal of providing a venue for interdisciplinary exchange of ideas on this emerging topic.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

Visual content creation is booming, yet producing engaging visual content remains a challenging task. This workshop aims to highlight machine learning technologies that accelerate and enhance creative processes in visual content creation and editing, including image animation, text-to-visual content generation, and content translation. Moreover, we believe that advancing technology to better understand edited visual content can enable novel platforms for creating compelling media. We seek to bridge the gap between technical and creative communities by bringing together researchers from computer vision, graphics, and the arts, fostering interdisciplinary collaboration and exploring opportunities in this under-explored area.

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

3D computer vision has become fundamental to technologies ranging from medical imaging to astronomy and from AR/VR to embodied intelligence. New sensors and imaging modalities like structured-light, time-of-flight, and light field microscopy are being developed to make 3D vision more tractable; but even with new types of sensor data, many problems in 3D vision tend to be ill-posed and hence to solve them we often rely on heuristics or data-driven priors. Unfortunately, these priors can fail in certain cases, especially for problems where ground truth data is not available, or for niche sensors where capturing large datasets is not feasible. A promising, but often overlooked, alternative is to incorporate knowledge of physics (e.g. physical light transport) into 3D computer vision algorithms, which can better constrain the solutions that they produce.

The goal of this workshop is to highlight work in 3D computer vision and imaging that makes use of physics-inspired modeling and physical-priors, showcasing their importance even with the prevalence of neural priors and big data. Examples include methods that apply physics-based approaches to inverse rendering, 3D microscopy, tomography, and light-in-flight imaging; or methods that combine such approaches with novel tools like neural radiance fields (NeRFs), 3D Gaussian Splatting (3DGS), and generative image/video models.