REVIEW 7 cited by
PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness. However, estimating physical material properties is an open problem due to the lack of material ground-truth data, as measuring these properties for real objects is highly difficult. We present PhysDreamer, a physics-based approach that endows static 3D objects with interactive dynamics by leveraging the object dynamics priors learned by video generation models. By distilling these priors, PhysDreamer enables the synthesis of realistic object responses to novel interactions, such as external forces or agent manipulations. We demonstrate our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study. PhysDreamer takes a step towards more engaging and realistic virtual experiences by enabling static 3D objects to dynamically respond to interactive stimuli in a physically plausible manner. See our project page at https://physdreamer.github.io/.
Forward citations
Cited by 7 Pith papers
-
Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties
ViWi uses material slots and a simulated RF descriptor to predict voxel-level Young's modulus, Poisson's ratio, and density, reporting gains over prior work on a synthetic benchmark.
-
Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.
-
TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos
TRACE predicts future frames of dynamic 3D scenes by learning a per-particle translation-rotation dynamics system inside 3D Gaussian Splatting, without labels.
-
Generative Physical AI in Vision: A Survey
A structured review that categorizes physics-aware generative models in vision into explicit-simulation and implicit-learning families and proposes six integration paradigms.
-
Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes
A text-driven pipeline that lifts 2D video diffusion motion into 3D Gaussian Splatting scenes via point tracking and depth estimation.
-
Realistic Surgical Simulation from Monocular Videos
SurgiSim reconstructs a canonical 3D Gaussian scene from a monocular surgical video and runs soft-tissue MPM simulations using viscoelastic parameters estimated by matching the video.
-
PhysID: Physics-based Interactive Dynamics from a Single-view Image
PhysID chains an MLLM, a single-image 3D reconstruction model, and Bullet physics to produce touch-interactive dynamics from one photo.
Discussion (0). Continue with ORCID to comment.