Human pose forecasting is inherently multimodal since multiple future motions exist for an observed pose sequence. However, learning this multimodality is challenging since the task is ill-posed. To address this issue, we propose an alternative paradigm to make the task well-posed. Additionally, while state-of-the-art methods predict multimodality, this is attained through a large volume of predictions obtained by oversampling. However, such an approach glosses over key questions: (1) Can we capture multimodality by efficiently sampling a smaller number of predictions? (2) Subsequently, which of the predicted futures is more likely for an observed pose sequence? We address these questions with MotionMap, a simple yet effective heatmap based representation for multimodality. We extend heatmaps to represent a spatial distribution over the space of all possible motions, where different local maxima correspond to different forecasts for a given observation. Not only can MotionMap capture a variable number of modes per observation, but it also provides confidence measures for different modes. Further, MotionMap captures rare modes that are non-trivial to evaluate yet critical for robustness. Finally, MotionMap allows us to introduce the notion of uncertainty and controllability over the forecasted pose sequence. We support our claims through multiple qualitative and quantitative experiments using popular 3D human pose datasets: Human3.6M and AMASS, highlighting the strengths and limitations of our proposed method.