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4K4D: Real-Time 4D View Synthesis at 4K Resolution

Zhen Xu · Sida Peng · Haotong Lin · Guangzhao He · Jiaming Sun · Yujun Shen · Hujun Bao · Xiaowei Zhou

Arch 4A-E Poster #48
[ ] [ Project Page ]
Fri 21 Jun 10:30 a.m. PDT — noon PDT

Abstract: This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recent methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports hardware rasterization and network pre-computation to enable unprecedented rendering speed with a high rendering quality. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition, we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover, we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30$\times$ faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at

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