Search Results for author: Xinhua Cheng

Found 8 papers, 4 papers with code

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators

2 code implementations7 Apr 2024 Shenghai Yuan, Jinfa Huang, Yujun Shi, Yongqi Xu, Ruijie Zhu, Bin Lin, Xinhua Cheng, Li Yuan, Jiebo Luo

Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.

Text-to-Video Generation Video Generation

Envision3D: One Image to 3D with Anchor Views Interpolation

1 code implementation13 Mar 2024 Yatian Pang, Tanghui Jia, Yujun Shi, Zhenyu Tang, Junwu Zhang, Xinhua Cheng, Xing Zhou, Francis E. H. Tay, Li Yuan

To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation.

Image to 3D

360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model

no code implementations12 Jan 2024 Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, Jian Zhang

Recently, the emerging text-to-video (T2V) diffusion methods demonstrate notable effectiveness in standard video generation.

Video Generation

Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting

1 code implementation20 Dec 2023 Junwu Zhang, Zhenyu Tang, Yatian Pang, Xinhua Cheng, Peng Jin, Yida Wei, Munan Ning, Li Yuan

The core idea is to combine the powerful image generation capability of the 2D diffusion model and the texture alignment ability of the repainting strategy for generating high-quality multi-view images with consistency.

3D Generation Image to 3D

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

no code implementations18 Oct 2023 Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li, Lei Zhang, Jian Zhang, Li Yuan

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies.

3D Generation Text to 3D

Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning

no code implementations CVPR 2023 Xinhua Cheng, Yanmin Wu, Mengxi Jia, Qian Wang, Jian Zhang

In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2D panoptic segmentation networks instead of the ground truth annotations.

Metric Learning Novel View Synthesis +1

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

2 code implementations CVPR 2023 Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, Jian Zhang

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues.

Object Sentence +1

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

no code implementations6 Jul 2021 Mengxi Jia, Xinhua Cheng, Shijian Lu, Jian Zhang

To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features.

Person Re-Identification Representation Learning

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