From Single Images to Motion Policies via Video-Generation Environment Representations

• URL: http://arxiv.org/abs/2505.19306v1
• Date: Sun, 25 May 2025 20:30:25 GMT
• Title: From Single Images to Motion Policies via Video-Generation Environment Representations
• Abstract summary: We propose a framework known as Video-Generation Environment Representation (VGER)<n>Frames of a moving camera video are input into a pre-trained 3D foundation model to produce a dense point cloud.<n>We then introduce a multi-scale noise approach to train an implicit representation of the environment structure and build a motion generation model that complies with the geometry of the representation.
• Score: 13.58353565350936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous robots typically need to construct representations of their surroundings and adapt their motions to the geometry of their environment. Here, we tackle the problem of constructing a policy model for collision-free motion generation, consistent with the environment, from a single input RGB image. Extracting 3D structures from a single image often involves monocular depth estimation. Developments in depth estimation have given rise to large pre-trained models such as DepthAnything. However, using outputs of these models for downstream motion generation is challenging due to frustum-shaped errors that arise. Instead, we propose a framework known as Video-Generation Environment Representation (VGER), which leverages the advances of large-scale video generation models to generate a moving camera video conditioned on the input image. Frames of this video, which form a multiview dataset, are then input into a pre-trained 3D foundation model to produce a dense point cloud. We then introduce a multi-scale noise approach to train an implicit representation of the environment structure and build a motion generation model that complies with the geometry of the representation. We extensively evaluate VGER over a diverse set of indoor and outdoor environments. We demonstrate its ability to produce smooth motions that account for the captured geometry of a scene, all from a single RGB input image.

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