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Found 10 papers, 4 papers with code
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI).
MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors
To overcome these limitations, we introduce MVDrag3D, a novel framework for more flexible and creative drag-based 3D editing that leverages multi-view generation and reconstruction priors.
ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance
Though promising results have been achieved in single object generation, these methods often struggle to model complex 3D assets that inherently contain multiple objects.
Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation
Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance.
Ranked #4 on
Text to 3D
on T$^3$Bench
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Creation of 3D content by stylization is a promising yet challenging problem in computer vision and graphics research.
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
In this work, we make the first attempt, to the best of our knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and propose a novel Masked Surfel Prediction (MaskSurf) method.
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
Such a challenge of Simulation-to-Reality (Sim2Real) domain gap could be mitigated via learning algorithms of domain adaptation; however, we argue that generation of synthetic point clouds via more physically realistic rendering is a powerful alternative, as systematic non-uniform noise patterns can be captured.
Universality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.
Deep Optimized Priors for 3D Shape Modeling and Reconstruction
Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples.
