3 citations · 6 across the 12 of their papers we have counts for
14 papers
Beyond Legibility: Benchmarking Visual Text Rendering and In-Place Editing in Unified Video Generation
Ziying Zhang, Litao Li, Junchao Liao +4
A video can exhibit convincing motion and photorealism yet fail immediately when visual text collapses. Unlike generic scene content, visual text is unforgiving in video generation…
CrowdGaussian: Reconstructing High-Fidelity 3D Gaussians for Human Crowd from a Single Image
Yizheng Song, Yiyu Zhuang, Qipeng Xu +5
Single-view 3D human reconstruction has garnered significant attention in recent years. Despite numerous advancements, prior research has concentrated on reconstructing 3D models f…
LaTo: Landmark-tokenized Diffusion Transformer for Fine-grained Human Face Editing
Zhenghao Zhang, Ziying Zhang, Junchao Liao +6
Recent multimodal models for instruction-based face editing enable semantic manipulation but still struggle with precise attribute control and identity preservation. Structural fac…
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
Shuang Wu, Youtian Lin, Feihu Zhang +8
Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges. We introduc…
Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors
Lin-Zhuo Chen, Kangjie Liu, Youtian Lin +4
3D Gaussian Splatting (3DGS) has achieved excellent rendering quality with fast training and rendering speed. However, its optimization process lacks explicit geometric constraints…
MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation
Shenhao Zhu, Lingteng Qiu, Xiaodong Gu +11
Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D metho…
Towards Native Generative Model for 3D Head Avatar
Yiyu Zhuang, Yuxiao He, Jiawei Zhang +6
Creating 3D head avatars is a significant yet challenging task for many applicated scenarios. Previous studies have set out to learn 3D human head generative models using massive 2…
EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head
Qianyun He, Xinya Ji, Yicheng Gong +11
We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality. Despite significant progress…
Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360°
Yuxiao He, Yiyu Zhuang, Yanwen Wang +6
Creating a 360° parametric model of a human head is a very challenging task. While recent advancements have demonstrated the efficacy of leveraging synthetic data for building such…
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Yifei Zeng, Yanqin Jiang, Siyu Zhu +6
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation. However, achieving high-fidelity 4D generation with spatial-temporal…
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
Shenhao Zhu, Junming Leo Chen, Zuozhuo Dai +6
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and mot…
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Youtian Lin, Zuozhuo Dai, Siyu Zhu +1
We introduce Gaussian-Flow, a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the…
AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
Zuozhuo Dai, Zhenghao Zhang, Yao Yao +4
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image. Recent image animation methods employ neural based rendering techn…
Improving Adversarial Transferability by Stable Diffusion
Jiayang Liu, Siyu Zhu, Siyuan Liang +4
Deep neural networks (DNNs) are susceptible to adversarial examples, which introduce imperceptible perturbations to benign samples, deceiving DNN predictions. While some attack met…