Ding-Kang Yang

Fysiverse-3D-Vision is proposed, a unified vision-language-geometry framework for generative 3D scene reconstruction and executable asset construction from a single image that establishes a shared representation where spatial reasoning and geometric reconstruction mutually enhance each other, allowing object layouts to be inferred beyond the constraints of individual asset generators.

OmniFysics-Nano-V2, a compact omni-modal model for physical-world perception and understanding, is presented and experiments show that the proposed data and training strategy improves physical-world understanding while preserving broad omni-modal competence.

SpatialGuard transforms 3D spatial text-to-image generation from a mere prompt-following exercise into a verifiable planning and validation process, significantly enhancing spatial accuracy.

PFAdapter cuts communication costs by nearly 50% while boosting accuracy in personalized federated learning for MLLMs.