← Search

Chun-Hao Paul Huang

7 accepted papers

2026

LoST: Level of Semantics Tokenization for 3D Shapes

CVPR 2026

Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation.However, optimal tokenization of 3D shapes remains an open question. S

Cited by 0SourcecodeScholar
2026

V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties

CVPR 2026

Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop framework that jointly understands intrinsic scene properties (e.g., albedo, normal, material, and irradiance), leverages t

Cited by 0SourcecodeScholar
2025

HUMOTO: A 4D Dataset of Mocap Human Object Interactions

ICCV 2025poster

We present Human Motions with Objects (HUMOTO), a high-fidelity dataset of human-object interactions for motion generation, computer vision, and robotics applications. Featuring 735 sequences (7,875 seconds at 30 fps), HUMOTO captures interactions with 63 precisely modeled objects and 72 articulated…

2025

Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions

CVPR 2025poster

We explore how body shapes influence human motion synthesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of learning a homogenized, canonical body shape. However, this homogenization can distort the natural correlations between different body shapes and t…

Cited by 1SourcePDFScholar
2024

ActAnywhere: Subject-Aware Video Background Generation

NeurIPS 2024poster

We study a novel problem to automatically generate video background that tailors to foreground subject motion. It is an important problem for the movie industry and visual effects community, which traditionally requires tedious manual efforts to solve. To this end, we propose ActAnywhere, a video di…

2024

Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

CVPR 2024poster

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry appearance motion and camera path. Creating computer-generated videos however is a tedious manual process which can be automated by emerging text-to-video diff…

Cited by 15SourcePDFScholar
2024

Synergistic Global-space Camera and Human Reconstruction from Videos

CVPR 2024poster

Remarkable strides have been made in reconstructing static scenes or human bodies from monocular videos. Yet the two problems have largely been approached independently without much synergy. Most visual SLAM methods can only reconstruct camera trajectories and scene structures up to scale while most…

Cited by 3SourcePDFScholar