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We present LTM3D, a Latent Token space Modeling framework for conditional 3D shape generation that integrates the strengths of diffusion and auto-regressive (AR) models. While diffusion-based methods effectively model continuous latent…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Xin Kang , Zihan Zheng , Lei Chu , Yue Gao , Jiahao Li , Hao Pan , Xuejin Chen , Yan Lu

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Huang , Tengfei Wang , Ziwei Liu , Qing Wang

Emerging Metaverse applications demand accessible, accurate, and easy-to-use tools for 3D digital human creations in order to depict different cultures and societies as if in the physical world. Recent large-scale vision-language advances…

Graphics · Computer Science 2023-04-07 Longwen Zhang , Qiwei Qiu , Hongyang Lin , Qixuan Zhang , Cheng Shi , Wei Yang , Ye Shi , Sibei Yang , Lan Xu , Jingyi Yu

Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge,…

Computer Vision and Pattern Recognition · Computer Science 2023-02-17 Omer Bar-Tal , Lior Yariv , Yaron Lipman , Tali Dekel

The task of audio-driven portrait animation involves generating a talking head video using an identity image and an audio track of speech. While many existing approaches focus on lip synchronization and video quality, few tackle the…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Jian Zhang , Weijian Mai , Zhijun Zhang

In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their…

Computer Vision and Pattern Recognition · Computer Science 2024-05-14 Taoran Yi , Jiemin Fang , Junjie Wang , Guanjun Wu , Lingxi Xie , Xiaopeng Zhang , Wenyu Liu , Qi Tian , Xinggang Wang

Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However, most existing works focus solely on the direct generation, ignoring the editing, restricting them from synthesizing…

Computer Vision and Pattern Recognition · Computer Science 2023-12-04 Yunjie Wu , Yapeng Meng , Zhipeng Hu , Lincheng Li , Haoqian Wu , Kun Zhou , Weiwei Xu , Xin Yu

In recent years, significant advancements have been made in text-driven 3D content generation. However, several challenges remain. In practical applications, users often provide extremely simple text inputs while expecting high-quality 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Huiqi Wu , Jianbo Mei , Yingjie Huang , Yining Xu , Jingjiao You , Yilong Liu , Li Yao

Motion transfer from the driving to the source portrait remains a key challenge in the portrait animation. Current diffusion-based approaches condition only on the driving motion, which fails to capture source-to-driving correspondences and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Yating Xu , Yunqi Miao , Evangelos Ververas , Jiankang Deng , Jifei Song

Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Yangguang Li , Zi-Xin Zou , Zexiang Liu , Dehu Wang , Yuan Liang , Zhipeng Yu , Xingchao Liu , Yuan-Chen Guo , Ding Liang , Wanli Ouyang , Yan-Pei Cao

In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Zeren Xiong , Zikun Chen , Zedong Zhang , Xiang Li , Ying Tai , Jian Yang , Jun Li

Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions…

Robotics · Computer Science 2026-01-01 Karthik Dharmarajan , Wenlong Huang , Jiajun Wu , Li Fei-Fei , Ruohan Zhang

Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction or non-rigid shape change. In this paper, we present an…

Computer Vision and Pattern Recognition · Computer Science 2024-02-14 Ta-Ying Cheng , Matheus Gadelha , Thibault Groueix , Matthew Fisher , Radomir Mech , Andrew Markham , Niki Trigoni

Motion-controllable video generation is crucial for egocentric applications in virtual reality and embodied AI. However, existing methods often struggle to achieve 3D-consistent fine-grained hand articulation. By adopting on 2D trajectories…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Chenyangguang Zhang , Botao Ye , Boqi Chen , Alexandros Delitzas , Fangjinhua Wang , Marc Pollefeys , Xi Wang

We propose a diffusion-based approach for Text-to-Image (T2I) generation with consistent and interactive 3D layout control and editing. While prior methods improve spatial adherence using 2D cues or iterative copy-warp-paste strategies,…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Andrea Rigo , Luca Stornaiuolo , Weijie Wang , Mauro Martino , Bruno Lepri , Nicu Sebe

Controllable generative models for images and videos have seen significant success, yet 3D scene generation, especially in unbounded scenarios like autonomous driving, remains underdeveloped. Existing methods lack flexible controllability…

Computer Vision and Pattern Recognition · Computer Science 2025-07-28 Ruiyuan Gao , Kai Chen , Zhihao Li , Lanqing Hong , Zhenguo Li , Qiang Xu

Textured 3D morphing creates smooth and plausible interpolation sequences between two 3D objects, focusing on transitions in both shape and texture. This is important for creative applications like visual effects in filmmaking. Previous…

Computer Vision and Pattern Recognition · Computer Science 2025-02-21 Songlin Yang , Yushi Lan , Honghua Chen , Xingang Pan

Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Amogh Joshi , Julian Ost , Felix Heide

3D scene generation has long been dominated by 2D multi-view or video diffusion models. This is due not only to the lack of scene-level 3D latent representation, but also to the fact that most scene-level 3D visual data exists in the form…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Dongxu Wei , Qi Xu , Zhiqi Li , Hangning Zhou , Cong Qiu , Hailong Qin , Mu Yang , Zhaopeng Cui , Peidong Liu

During the early stages of interface design, designers need to produce multiple sketches to explore a design space. Design tools often fail to support this critical stage, because they insist on specifying more details than necessary.…

Human-Computer Interaction · Computer Science 2026-03-31 Aryan Garg , Yue Jiang , Antti Oulasvirta