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Found 15 papers, 7 papers with code
CT4D: Consistent Text-to-4D Generation with Animatable Meshes
The primary challenges of our mesh-based framework involve stably generating a mesh with details that align with the text prompt while directly driving it and maintaining surface continuity.
CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner.
Taming Diffusion Probabilistic Models for Character Control
We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals.
SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D
Therefore, we improve the consistency by aligning the 2D geometric priors in diffusion models with well-defined 3D shapes during the lifting, addressing the vast majority of the problem.
C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters.
LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation
While previous methods are able to generate speech rhythm-synchronized gestures, the semantic context of the speech is generally lacking in the gesticulations.
Example-based Motion Synthesis via Generative Motion Matching
At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches.
Patch-based 3D Natural Scene Generation from a Single Example
We target a 3D generative model for general natural scenes that are typically unique and intricate.
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost.
SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene
We present a 3D generative model for general natural scenes.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras
In this paper, we introduce Neural Motion Consensus Flow (MoCo-Flow), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time-variant function.
Towards a Neural Graphics Pipeline for Controllable Image Generation
NGP decomposes the image into a set of interpretable appearance feature maps, uncovering direct control handles for controllable image generation.
Multimodal Shape Completion via Conditional Generative Adversarial Networks
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i. e., filling the regions that were missing in the shape.
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i. e., plausibly filling in regions there were missed in the raw scans.
