PhysGen3D: Crafting a Miniature Interactive World from a Single Image

• URL: http://arxiv.org/abs/2503.20746v1
• Date: Wed, 26 Mar 2025 17:31:04 GMT
• Title: PhysGen3D: Crafting a Miniature Interactive World from a Single Image
• Authors: Boyuan Chen, Hanxiao Jiang, Shaowei Liu, Saurabh Gupta, Yunzhu Li, Hao Zhao, Shenlong Wang,
• Abstract summary: PhysGen3D is a novel framework that transforms a single image into an amodal, camera-centric, interactive 3D scene.<n>At its core, PhysGen3D estimates 3D shapes, poses, physical and lighting properties of objects.<n>We evaluate PhysGen3D's performance against closed-source state-of-the-art (SOTA) image-to-video models, including Pika, Kling, and Gen-3.
• Score: 31.41059199853702
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce PhysGen3D, a novel framework that transforms a single image into an amodal, camera-centric, interactive 3D scene. By combining advanced image-based geometric and semantic understanding with physics-based simulation, PhysGen3D creates an interactive 3D world from a static image, enabling us to "imagine" and simulate future scenarios based on user input. At its core, PhysGen3D estimates 3D shapes, poses, physical and lighting properties of objects, thereby capturing essential physical attributes that drive realistic object interactions. This framework allows users to specify precise initial conditions, such as object speed or material properties, for enhanced control over generated video outcomes. We evaluate PhysGen3D's performance against closed-source state-of-the-art (SOTA) image-to-video models, including Pika, Kling, and Gen-3, showing PhysGen3D's capacity to generate videos with realistic physics while offering greater flexibility and fine-grained control. Our results show that PhysGen3D achieves a unique balance of photorealism, physical plausibility, and user-driven interactivity, opening new possibilities for generating dynamic, physics-grounded video from an image.

Related papers

• SOPHY: Generating Simulation-Ready Objects with Physical Materials [10.156212838002903]
  SOPHY is a generative model for 3D physics-aware shape synthesis. Our approach jointly synthesizes shape, texture, and material properties related to physics-grounded dynamics.
  arXiv  Detail & Related papers  (2025-04-17T06:17:24Z)
• PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos [21.441062722848265]
  PhysTwin is a novel framework that uses sparse videos of dynamic objects under interaction to produce a photo- and physically realistic, real-time interactive replica. Our approach centers on two key components: (1) a physics-informed representation that combines spring-mass models for realistic physical simulation, and generative shape models for geometry, and Gaussian splats for rendering. Our method integrates an inverse physics framework with visual perception cues, enabling high-fidelity reconstruction even from partial, occluded, and limited viewpoints.
  arXiv  Detail & Related papers  (2025-03-23T07:49:19Z)
• Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation [47.6666060652434]
  We present an innovative framework that generates 3D models with accurate appearances and geometric structures.<n>By integrating text-to-3D generation with physics-grounded motion synthesis, our framework renders photo-realistic 3D objects.
  arXiv  Detail & Related papers  (2024-12-07T06:48:16Z)
• Phys4DGen: A Physics-Driven Framework for Controllable and Efficient 4D Content Generation from a Single Image [3.131272328696594]
  Existing methods rely heavily on pre-trained video diffusion models to guide 4D content dynamics.<n>We propose Phys4DGen, a novel framework that generates physics-compliant 4D content from a single image.<n>Inspired by the human ability to infer physical properties visually, we introduce a Physical Perception Module.
  arXiv  Detail & Related papers  (2024-11-25T12:12:38Z)
• PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation [29.831214435147583]
  We present PhysGen, a novel image-to-video generation method. It produces a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with a data-driven video generation process.
  arXiv  Detail & Related papers  (2024-09-27T17:59:57Z)
• Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video [58.043569985784806]
  We introduce latent intuitive physics, a transfer learning framework for physics simulation. It can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes. We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation.
  arXiv  Detail & Related papers  (2024-06-18T16:37:44Z)
• Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion [35.71595369663293]
  We propose textbfPhysics3D, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model. Experiments demonstrate the effectiveness of our method with both elastic and plastic materials.
  arXiv  Detail & Related papers  (2024-06-06T17:59:47Z)
• DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors [75.83647027123119]
  We propose to learn the physical properties of a material field with video diffusion priors.<n>We then utilize a physics-based Material-Point-Method simulator to generate 4D content with realistic motions.
  arXiv  Detail & Related papers  (2024-06-03T16:05:25Z)
• Physically Compatible 3D Object Modeling from a Single Image [109.98124149566927]
  We present a framework that transforms single images into 3D physical objects.<n>Our framework embeds physical compatibility into the reconstruction process.<n>It consistently enhances the physical realism of 3D models over existing methods.
  arXiv  Detail & Related papers  (2024-05-30T21:59:29Z)
• PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation [62.53760963292465]
  PhysDreamer is a physics-based approach that endows static 3D objects with interactive dynamics. We present our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study.
  arXiv  Detail & Related papers  (2024-04-19T17:41:05Z)
• PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations [62.14943588289551]
  We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a human. PhysAvatar reconstructs avatars dressed in loose-fitting clothes under motions and lighting conditions not seen in the training data.
  arXiv  Detail & Related papers  (2024-04-05T21:44:57Z)

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.