64 skills found · Page 1 of 3
walsvid / Pixel2MeshPlusPlus[ICCV2019] Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation.
mickare / Deformation Transfer For Triangle MeshesA Python implementation of the paper "Deformation Transfer for Triangle Meshes" with 3D views in the browser.
yifita / Deep Cagecode for "Neural Cages for Detail-Preserving 3D Deformations"
JeongminB / E D3DGS[ECCV 2024] Official repository for "Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting"
mks0601 / PERSONA RELEASEOfficial PyTorch implementation of "PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image", ICCV 2025.
AutoVision-cloud / Deformable PV RCNN[ECCVW-2020] Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
laughtervv / 3DN3DN: 3D Deformation Network, CVPR 2019
facebookresearch / VCMeshConvLearning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.
jyunlee / Pop Out MotionPop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)
Colin97 / DeepMetaHandlesDeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
alexbourgeois / OBNI3DGraphical pipeline allowing mesh deformation through shader with 3D noise volumes
ShivamDuggal4 / TARS3DTopologically-Aware Deformation Fields for Single-View 3D Reconstruction (CVPR 2022)
cheind / Mesh Deform:lollipop: Physically plausible interactive 3D mesh deformation based on as rigid as possible constraints.
jackd / Template FfdCode for paper "Learning Free-Form Deformations for 3D Object Reconstruction"
jjacobson / Realtime SnowRealtime snow deformation & accumulation in unity 3d
optas / Changeit3dOfficial pytorch code for "ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations"
vasiliskatr / Deformation Transfer ARkit BlendshapesImplementation of the deformation transfer paper and its application in generating all the ARkit facial blend shapes for any 3D face
YuQiao0303 / Fancy123[CVPR2025]Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
maxjiang93 / ShapeFlowLearnable Deformations Among 3D Shapes.
Myzhencai / Total Capturesome source code link for the paper:Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
