This applies to common objects such as chairs, but also to novel objects that wehaveneverseenbefore.

Thus, they do not effectively capture implicit correspondences between articulated shapes or regularize jittery temporal deformations.

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.

We propose a new representation for encoding 3D shapes as neural fields. The representation isdesignedtobecompatible withthetransformer architecture and to benefit both shape reconstruction and shape generation. Existing works on neural fields aregrid-based representations withlatents defined onaregular grid.

We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image, focusing on part-level shape reconstruction and pose and kinematics estimation.

Our method utilizes animage-conditioned 3Dscene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene.

Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.

Section 2, Section 3 and Section 4 provide more visualization results on a number of 3D modelingtasks,includingshapereconstruction,generationandinterpolation. Note that all the hierarchical aggregators {Ei} share the same networkparameters. Hierarchical decoder D includes an implicit octant decoder and some hierarchical local decoders {Di}, which maps the decoded shape code and a sample point (x,y,z) to the local geometry and the local latent feature, respectively. IN means the instance normalization 3D operator[9]. We provide two tables (Table 1 and Table 2) to detail the network structures of 3D voxel encoder andimplicitdecoder,respectively.