6 citations · 24 across the 20 of their papers we have counts for
22 papers
Pair-Free Coarse-to-Real Video Generation via Real-to-Coarse Image Supervision
Gonzalo Gómez-Nogales, Daohan Lu, Peiye Zhuang +2
Coarse-to-real (C2R) video generation converts coarse animation, simulation, or game-rendered videos into photorealistic videos while preserving scene layout, camera motion, and dy…
CubePart: An Open-Vocabulary Part-Controllable 3D Generator
Yiheng Zhu, Kangle Deng, Jean-Philippe Fauconnier +9
Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted behaviors, yet most generative…
T2Bs: Text-to-Character Blendshapes via Video Generation
Jiahao Luo, Chaoyang Wang, Michael Vasilkovsky +8
We present T2Bs, a framework for generating high-quality, animatable character head morphable models from text by combining static text-to-3D generation with video diffusion. Text-…
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation
Himangi Mittal, Peiye Zhuang, Hsin-Ying Lee +1
We propose UniPhy, a common latent-conditioned neural constitutive model that can encode the physical properties of diverse materials. At inference UniPhy allows `inverse simulatio…
Cube: A Roblox View of 3D Intelligence
Foundation AI Team, Kiran Bhat, Nishchaie Khanna +44
Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Ro…
PrEditor3D: Fast and Precise 3D Shape Editing
Ziya Erkoç, Can Gümeli, Chaoyang Wang +5
We propose a training-free approach to 3D editing that enables the editing of a single shape within a few minutes. The edited 3D mesh aligns well with the prompts, and remains iden…
4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion
Chaoyang Wang, Peiye Zhuang, Tuan Duc Ngo +7
We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sh…
DELTA: Dense Efficient Long-range 3D Tracking for any video
Tuan Duc Ngo, Peiye Zhuang, Chuang Gan +4
Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method that…
Pixel-Aligned Multi-View Generation with Depth Guided Decoder
Zhenggang Tang, Peiye Zhuang, Chaoyang Wang +5
The task of image-to-multi-view generation refers to generating novel views of an instance from a single image. Recent methods achieve this by extending text-to-image latent diffus…
4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models
Heng Yu, Chaoyang Wang, Peiye Zhuang +6
Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As…
GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement
Peiye Zhuang, Songfang Han, Chaoyang Wang +7
We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based t…
AToM: Amortized Text-to-Mesh using 2D Diffusion
Guocheng Qian, Junli Cao, Aliaksandr Siarohin +12
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods…
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Chaoyang Wang, Peiye Zhuang, Aliaksandr Siarohin +4
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, parti…
SceneWiz3D: Towards Text-guided 3D Scene Composition
Qihang Zhang, Chaoyang Wang, Aliaksandr Siarohin +7
We are witnessing significant breakthroughs in the technology for generating 3D objects from text. Existing approaches either leverage large text-to-image models to optimize a 3D r…
HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
Junzhe Zhu, Peiye Zhuang, Sanmi Koyejo
The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like N…
Diffusion Probabilistic Fields
Peiye Zhuang, Samira Abnar, Jiatao Gu +3
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative…
Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging
Peiye Zhuang, Bliss Chapman, Ran Li +1
In the experimental sciences, statistical power analyses are often used before data collection to determine the required sample size. However, traditional power analyses can be cos…
AMICO: Amodal Instance Composition
Peiye Zhuang, Jia-bin Huang, Ayush Saraf +3
Image composition aims to blend multiple objects to form a harmonized image. Existing approaches often assume precisely segmented and intact objects. Such assumptions, however, are…
Controllable Radiance Fields for Dynamic Face Synthesis
Peiye Zhuang, Liqian Ma, Oluwasanmi Koyejo +1
Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering. However, 3D-aware synthesis of face dynamics hasn't received much attent…
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
Peiye Zhuang, Oluwasanmi Koyejo, Alexander G. Schwing
Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e.g., gradually making a summer scene look like it was taken in winter. Clas…
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
Siddharth Biswal, Peiye Zhuang, Ayis Pyrros +3
Deep generative models have enabled the automated synthesis of high-quality data for diverse applications. However, the most effective generative models are specialized to data fro…
FMRI data augmentation via synthesis
Peiye Zhuang, Alexander G. Schwing, Sanmi Koyejo
We present an empirical evaluation of fMRI data augmentation via synthesis. For synthesis we use generative mod-els trained on real neuroimaging data to produce novel task-dependen…