Flex3D features a two-stage process that generates high-quality 3D assets from a single image or text prompt. This technology represents the latest advancements in the field of 3D reconstruction, significantly improving the efficiency and quality of 3D content generation. Flex3D is developed with support from Meta, and the team has a strong background in 3D reconstruction and computer vision.

DressCode is a text-driven framework for 3D clothing generation, designed to democratize design for beginners and unleash tremendous potential for fashion design, virtual fitting, and digital human creation. It initially introduces SewingGPT, a GPT-based architecture that integrates cross-attention and text-conditioned embeddings for generating sewing patterns from text instructions. Next, it customizes a pre-trained Stable Diffusion to create physically based rendering (PBR) textures based on tiles. By leveraging large language models, this framework generates CG-friendly clothing through natural language interactions, facilitates pattern completion and texture editing, and simplifies the design process through a user-friendly interface.

IMAGDressing is an interactive modular clothing generation model designed to provide flexible and controllable customization services for virtual fitting systems. The model integrates the semantic features of CLIP and the texture features of VAE using a mixed attention module and integrates them into the denoising UNet, ensuring that users can control the editing. In addition, IMAGDressing also provides the IGPair dataset containing over 300,000 pairs of clothing and wearing images, establishing a standard data assembly process. This model can be used in conjunction with ControlNet, IP-Adapter, T2I-Adapter, and AnimateDiff, enhancing diversity and controllability.

EgoGaussian is an advanced 3D scene reconstruction and dynamic object tracking technology. It can reconstruct 3D scenes and track the movement of objects dynamically using only RGB first-person perspective input. This technology leverages the unique discrete properties of Gaussian scattering to segment dynamic interactions from the background. Through a piece-wise online learning process, it utilizes the dynamic characteristics of human activities to reconstruct the evolution of the scene in chronological order and track the movement of rigid objects. EgoGaussian outperforms previous NeRF and dynamic Gaussian methods in wild video challenges and delivers exceptional quality in reconstructed models.

GaussianCube is an innovative 3D radiance representation method that significantly advances 3D generative modeling through its structured and explicit representation. This technology achieves high-precision fitting by utilizing a novel density-constrained Gaussian fitting algorithm and optimal transport methods, rearranging Gaussian functions onto a predefined voxel grid. Compared to traditional implicit feature decoders or spatially unstructured radiance representations, GaussianCube boasts fewer parameters and higher quality, making 3D generative modeling more accessible.

Developed by a team from Tsinghua University, Unique3D is a technology that can generate high-fidelity textured 3D mesh models from a single image. This technology has significant implications for image processing and 3D modeling, enabling users to quickly convert 2D images into 3D models, providing powerful technical support for game development, animation production, and virtual reality.

VastGaussian is an open-source project for 3D scene reconstruction that leverages 3D Gaussians to model the geometric and appearance information of large-scale scenes. This project is an implementation from scratch and may contain some errors, but it offers a new perspective for the field of 3D scene reconstruction. Key advantages include its ability to process large datasets and the improvements made to the original 3DGS project, enhancing its clarity and usability.

PhysDreamer is a method based on physics, which endows静态 3D objects with interactive dynamics by utilizing the object dynamics prior learned from video generation models. This approach allows for the simulation of realistic responses to novel interactions (such as external forces or agent operations) in the absence of real physical property data of objects. PhysDreamer promotes the development of more engaging and realistic virtual experiences through user studies to evaluate the realism of synthetic interactions.

Interactive3D is an advanced 3D generation model that provides users with precise control capabilities through interactive design. The model utilizes a two-stage cascading structure, employing different 3D representation methods, allowing users to modify and guide at any intermediate step of the generation process. Its significance lies in the ability to achieve fine control over the 3D model generation process, thereby creating high-quality 3D models that meet specific requirements.

MakerLab is an online platform offering a variety of 3D model design tools, including vase generators and sign customizers. Users can quickly and easily create personalized 3D models based on their own needs. The platform supports the use of templates for creation and provides an innovation playground where users can experiment with cutting-edge technologies like AI scanners. MakerLab is operated by BamBam Lab and aims to provide a space for users to freely create and share their ideas. Currently, the platform offers both free and paid services for users to choose according to their needs.

Flex3D features a two-stage process that generates high-quality 3D assets from a single image or text prompt. This technology represents the latest advancements in the field of 3D reconstruction, significantly improving the efficiency and quality of 3D content generation. Flex3D is developed with support from Meta, and the team has a strong background in 3D reconstruction and computer vision.

DressCode is a text-driven framework for 3D clothing generation, designed to democratize design for beginners and unleash tremendous potential for fashion design, virtual fitting, and digital human creation. It initially introduces SewingGPT, a GPT-based architecture that integrates cross-attention and text-conditioned embeddings for generating sewing patterns from text instructions. Next, it customizes a pre-trained Stable Diffusion to create physically based rendering (PBR) textures based on tiles. By leveraging large language models, this framework generates CG-friendly clothing through natural language interactions, facilitates pattern completion and texture editing, and simplifies the design process through a user-friendly interface.

IMAGDressing is an interactive modular clothing generation model designed to provide flexible and controllable customization services for virtual fitting systems. The model integrates the semantic features of CLIP and the texture features of VAE using a mixed attention module and integrates them into the denoising UNet, ensuring that users can control the editing. In addition, IMAGDressing also provides the IGPair dataset containing over 300,000 pairs of clothing and wearing images, establishing a standard data assembly process. This model can be used in conjunction with ControlNet, IP-Adapter, T2I-Adapter, and AnimateDiff, enhancing diversity and controllability.

EgoGaussian is an advanced 3D scene reconstruction and dynamic object tracking technology. It can reconstruct 3D scenes and track the movement of objects dynamically using only RGB first-person perspective input. This technology leverages the unique discrete properties of Gaussian scattering to segment dynamic interactions from the background. Through a piece-wise online learning process, it utilizes the dynamic characteristics of human activities to reconstruct the evolution of the scene in chronological order and track the movement of rigid objects. EgoGaussian outperforms previous NeRF and dynamic Gaussian methods in wild video challenges and delivers exceptional quality in reconstructed models.

GaussianCube is an innovative 3D radiance representation method that significantly advances 3D generative modeling through its structured and explicit representation. This technology achieves high-precision fitting by utilizing a novel density-constrained Gaussian fitting algorithm and optimal transport methods, rearranging Gaussian functions onto a predefined voxel grid. Compared to traditional implicit feature decoders or spatially unstructured radiance representations, GaussianCube boasts fewer parameters and higher quality, making 3D generative modeling more accessible.

Developed by a team from Tsinghua University, Unique3D is a technology that can generate high-fidelity textured 3D mesh models from a single image. This technology has significant implications for image processing and 3D modeling, enabling users to quickly convert 2D images into 3D models, providing powerful technical support for game development, animation production, and virtual reality.

VastGaussian is an open-source project for 3D scene reconstruction that leverages 3D Gaussians to model the geometric and appearance information of large-scale scenes. This project is an implementation from scratch and may contain some errors, but it offers a new perspective for the field of 3D scene reconstruction. Key advantages include its ability to process large datasets and the improvements made to the original 3DGS project, enhancing its clarity and usability.

PhysDreamer is a method based on physics, which endows静态 3D objects with interactive dynamics by utilizing the object dynamics prior learned from video generation models. This approach allows for the simulation of realistic responses to novel interactions (such as external forces or agent operations) in the absence of real physical property data of objects. PhysDreamer promotes the development of more engaging and realistic virtual experiences through user studies to evaluate the realism of synthetic interactions.