arXiv Computer Vision By Bei Huang, Yixin Chen, Hongbin Zha, Yuru Pei, Siyuan Huang

AutoPhyX: Automatic Text-Condition Physics Property Generation

Read the original on arXiv Computer Vision →

AutoPhyX is a text-conditioned framework that predicts spatially-varying physical properties for 4D physical simulation. It uses a novel part-controllable data generation pipeline to create fine-grained, semantically distinct 3D assets paired with diverse parameters and text descriptions. The model employs cross-modal modulation to dynamically adjust visual features based on text, enabling accurate, physically plausible parameter assignment across various 3D representations in a single forward pass.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers
Jul 21

Learning Explicit Physical Parameter Control and Benchmarking for Video Generation

Recent advances in image-to-video generation have improved visual realism, making physically grounded and controllable dynamics an important step toward future world simulation. Current models often generate plausible motion, but it is not reliably governed by explicit physical causes, and instance-level constraints can leak or become entangled in multi-object interactions.

arXiv Computer Vision
Oct 19

FlexPart: Text-Controlled Semantics-Aware 3D Part Segmentation at Flexible Granularity

FlexPart is a new framework that enables text‑controlled, semantics‑aware 3D mesh segmentation at any desired granularity. It introduces a holistic control prompt that combines part names with location and shape clauses, and uses a Vision‑Language Model pipeline to generate a large multi‑granularity dataset. The model produces a view‑independent part‑identity field in 3D space, and an inference‑time VLM planner can auto‑complete descriptive clauses from 2D renderings, allowing users to specify parts with ordinary words. FlexPart outperforms strong baselines by up to 22% in mIoU on its benchmark and improves selective segmentation by 35% over click‑driven methods.

By Yiqun Zhao, Jiachen Xu, Yihao Zhi, Yunfei Zhao, Zibo Zhao, Xintong Han, Chunchao Guo, Shenghua Gao