Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Realistic object interactions are crucial for creating immersive virtual experiences, yet
synthesizing realistic 3D object dynamics in response to novel interactions remains a …

As 3D content becomes increasingly prevalent, there'sa growing focus on the development
of engagements with 3D virtual content. Unfortunately, traditional techniques for creating …

The proliferation of 3D representations, from explicit meshes to implicit neural fields and
more, motivates the need for simulators agnostic to representation. We present a data …

This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from …

This paper studies the problem of estimating physical properties (system identification)
through visual observations. To facilitate geometry-aware guidance in physical property …

Neural Fields have emerged as a transformative approach for 3D scene representation in
computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and …

While neural radiance fields (NeRF) have shown promise in novel view synthesis, their
implicit representation limits explicit control over object manipulation. Existing research has …

We present ElastoGen, a knowledge-driven model that generates physically accurate and
coherent 4D elastodynamics. Instead of relying on petabyte-scale data-driven learning …

While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI
systems often struggle. Current methods for visual grounding of dynamics either use pure …