Seg3DParts: Segmentation-Grounded Controllable Part-Level 3D Generation

arXiv:2609.36918 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Transformer — 16× in this paper · explained in 2 episodes
• Generative Model — 12× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Fidelity — 10× in this paper · explained in 2 episodes
• Generative Models — 10× in this paper · explained in 2 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Latent Space — 4× in this paper · explained in 7 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Chamfer Distance — 3× in this paper
• Latent Representations — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• ACM Transactions On Graphics — 1× in this paper
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• HoloPart: Generative 3D Part Amodal Segmentation
• X-Part: high fidelity and structure coherent shape decomposition
• AutoPartGen: Autogressive 3D Part Generation and Discovery
• PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
• SAMPart3D: Segment Any Part in 3D Objects
• PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers
• From One to More: Contextual Part Latents for 3D Generation
• Efficient Part-level 3D Object Generation via Dual Volume Packing
• FullPart: Generating each 3D Part at Full Resolution
• Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
• Wonder3D: Single Image to 3D using Cross-Domain Diffusion
• UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
• Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
• Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
• TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
• Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling
• MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
• GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
• MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation
• OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models