DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness

• URL: http://arxiv.org/abs/2503.22677v1
• Date: Fri, 28 Mar 2025 17:59:53 GMT
• Title: DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness
• Authors: Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi,
• Abstract summary: Most 3D object generators focus on aesthetic quality, neglecting physical constraints necessary in applications.<n>One such constraint is that the 3D object should be self-supporting, i.e., remains balanced under gravity.<n>We propose Direct Simulation Optimization (DSO), a framework to use the feedback from a (non-differentiable) simulator to increase the likelihood that the 3D generator outputs stable 3D objects directly.
• Score: 67.97235923372035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most 3D object generators focus on aesthetic quality, often neglecting physical constraints necessary in applications. One such constraint is that the 3D object should be self-supporting, i.e., remains balanced under gravity. Prior approaches to generating stable 3D objects used differentiable physics simulators to optimize geometry at test-time, which is slow, unstable, and prone to local optima. Inspired by the literature on aligning generative models to external feedback, we propose Direct Simulation Optimization (DSO), a framework to use the feedback from a (non-differentiable) simulator to increase the likelihood that the 3D generator outputs stable 3D objects directly. We construct a dataset of 3D objects labeled with a stability score obtained from the physics simulator. We can then fine-tune the 3D generator using the stability score as the alignment metric, via direct preference optimization (DPO) or direct reward optimization (DRO), a novel objective, which we introduce, to align diffusion models without requiring pairwise preferences. Our experiments show that the fine-tuned feed-forward generator, using either DPO or DRO objective, is much faster and more likely to produce stable objects than test-time optimization. Notably, the DSO framework works even without any ground-truth 3D objects for training, allowing the 3D generator to self-improve by automatically collecting simulation feedback on its own outputs.

Related papers

• DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
  We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets. Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
  arXiv  Detail & Related papers  (2024-12-11T07:32:17Z)
• Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors [17.544733016978928]
  3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Recent advancements in 3D object generation have introduced techniques that reconstruct an object's 3D shape and texture. We propose bridging the gap between 2D and 3D diffusion models to address this limitation.
  arXiv  Detail & Related papers  (2024-10-12T10:14:11Z)
• DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
  We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts. Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication [50.541882834405946]
  We introduce Atlas3D, an automatic and easy-to-implement text-to-3D method. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-05-28T18:33:18Z)
• ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance [76.7746870349809]
  We present ComboVerse, a 3D generation framework that produces high-quality 3D assets with complex compositions by learning to combine multiple models. Our proposed framework emphasizes spatial alignment of objects, compared with standard score distillation sampling.
  arXiv  Detail & Related papers  (2024-03-19T03:39:43Z)
• L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects [53.4874127399702]
  We propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation. We develop a new benchmark, Unconventionally Feasible Objects (UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender. Our approach surpasses the standard GPT-4 and other language agents for 3D mesh generation on ShapeNet.
  arXiv  Detail & Related papers  (2024-02-14T09:51:05Z)
• Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors [16.93758384693786]
  Bidirectional Diffusion(BiDiff) is a unified framework that incorporates both a 3D and a 2D diffusion process. Our model achieves high-quality, diverse, and scalable 3D generation.
  arXiv  Detail & Related papers  (2023-12-07T10:00:04Z)
• Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity [3.359622001455893]
  Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by chamfer distance or voxel IOU. We propose ARM, an amodal 3D reconstruction system that introduces a stability prior over object shapes, (2) a connectivity prior, and (3) a multi-channel input representation.
  arXiv  Detail & Related papers  (2020-09-28T08:52:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.