Multi-view consistency remains a challenge for image diffusion models. Even within the Text-to-Texture problem, where perfect geometric correspondences are known a priori, many methods fail to yield aligned predictions across views, necessitating non-trivial fusion methods to incorporate the results onto the original mesh. We explore this issue for a Collaborative Control workflow specifically in PBR Text-to-Texture. Collaborative Control directly models PBR image probability distributions, including normal bump maps; to our knowledge, the only diffusion model to directly output full PBR stacks. We discuss the design decisions involved in making this model multi-view consistent, and demonstrate the effectiveness of our approach in ablation studies, as well as practical applications.
PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息处理国家重点实验室，计算机学院，北京大学）；National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（视觉技术国家工程研究中心，计算机学院，北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Light Illusions；PKU-AI 2 Robotics Joint Lab of Embodied AI（北京大学人工智能2机器人联合实验室）
Zhejiang University（浙江大学）；Shanghai Innovation Institute（上海创新研究院）；Westlake University（西湖大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Fudan University（复旦大学）
DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation
PCA Lab, VCIP, College of Computer Science, Nankai University（南开大学计算机学院VCIP实验室PCA Lab）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；Horizon Robotics（地平线机器人）；D-Robotics
ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Pengcheng Laboratory, China（鹏城实验室）；Harbin Institute of Technology, China（哈尔滨工业大学）；Harbin Institute of Technology, Suzhou Research Institute, China（哈尔滨工业大学苏州市研究院）
Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering
University of Tübingen（蒂宾根大学）；Tübingen AI Center（蒂宾根人工智能中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；POSTECH（浦项科技大学）；Institute for Convergence Research and Education in Advanced Technology, Yonsei University（延世大学尖端技术融合研究与教育学院）
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Baidu VIS（百度VIS）；Institute of Medical Technology, Peking University（北京大学医学技术研究所）
MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Tencent Hunyuan（腾讯文元）；The Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
Automatic generation of artistic glyph images is a challenging task that attracts many research interests. Previous methods either are specifically designed for shape synthesis or focus on texture transfer. In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. To achieve this goal, we first disentangle the representations for content and style by using two encoders, ensuring the multi-content and multi-style generation. Then we utilize two collaboratively working decoders to generate the glyph shape image and…
DAC-Pose: Dual-Agent Collaborative Framework for Pose-Guided Human Generation
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Shenzhen University of Advanced Technology（深圳理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
Temasek Life Sciences Laboratory（淡马锡生命科学研究院）；National University of Singapore（新加坡国立大学）；Entopsis LLC（Entopsis有限责任公司）；Uppsala University（乌普萨拉大学）
Distance measures are part and parcel of many computer vision algorithms. The underlying assumption in all existing distance measures is that feature elements are independent and identically distributed. However, in real-world settings, data generally originate from heterogeneous sources even if they do possess a common data-generating mechanism. Since these sources are not identically distributed by necessity, the assumption of identical distribution is inappropriate. Here, we use statistical analysis to show that feature elements of local image descriptors are indeed non-identically distribu…
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations which infer 3D texture distribution, our method explicitly constrains the texture distribution on the 2D surface of the template as well as avoids limitations of fixed resolution and topology. Moreover, by fusing the global and local feature…
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）
This paper introduces a simple but highly efficient ensemble for robust texture classification, which can effectively deal with translation, scale and changes of significant viewpoint problems. The proposed method first inherits the spirit of spatial pyramid matching model (SPM), which is popular for encoding spatial distribution of local features, but in a flexible way, partitioning the original image into different levels and incorporating different overlapping patterns of each level. This flexible setup helps capture the informative features and produces sufficient local feature codes by so…
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
S-Lab, Nanyang Technological University（南洋理工大学S-Lab）；Peking University（北京大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based rendering (PBR), necessary for 3D asset relighting in real-world applications. Additionally, 3DGen supports generative retexturing of previously generated (or artist-created) 3D shapes using additional textual inputs provided by the user. 3DGen integrates key technical components, Meta 3D AssetGen and Meta 3D TextureGen, that we developed for text-…
Jiangnan University（江南大学）；University of Surrey（萨里大学）；Anyvision
The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification. To this end, we first fit a 3DMM to the 2D face images of a dictionary to reconstruct the 3D shape and texture of each image. The 3D faces are used to render a number of virtual 2D face images with arbitrary pose variations to augment the training data, by merging the original and rendered virtual samples to create an extended dictionary. Second, to reduce the information redundancy of the extended dictionary and improve the spar…
FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow
School of Computer Science, Peking University（北京大学计算机科学系）；Technical University of Munich（慕尼黑技术大学）；Beijing Jiaotong University（北京交通大学）；Beijing Digital Native Digital City Research Center（北京数字原生数字城市研究院）；Theta Labs, Inc.（Theta Labs公司）；Beijing Normal University（北京师范大学）
