Scaling Versatile 3D Assets Editing with a Million-Scale Dataset

arXiv:2609.34271 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/chaofengc/IQA-PyTorch

Terminology

• Benchmark — 33× in this paper · explained in 2 episodes
• Alignment — 31× in this paper · explained in 1 episode
• Distillation — 12× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• Generative Model — 8× in this paper · explained in 2 episodes
• Identity Preservation — 7× in this paper
• CVF International Conference on Computer Vision — 6× in this paper
• Generative Models — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Instruction Following — 5× in this paper
• Visual Quality — 5× in this paper
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Success Rate — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Distribution Matching — 3× in this paper · explained in 2 episodes
• Multimodal Large Language Model — 3× in this paper
• Alignment Scores — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Evaluation Dimensions — 2× in this paper · explained in 3 episodes
• Gaussian Splatting — 2× in this paper · explained in 4 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• Qwen3-VL Technical Report
• HunyuanImage 3.0 Technical Report
• FullPart: Generating each 3D Part at Full Resolution
• Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
• MUSIQ: Multi-scale Image Quality Transformer
• Auto-Encoding Variational Bayes
• SegviGen: Repurposing 3D Generative Model for Part Segmentation
• Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing
• TanGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization
• Feedforward 3D Editing via Text-Steerable Image-to-3D
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts
• GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian Splatting
• EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection
• SAM 2: Segment Anything in Images and Videos
• Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
• DINOv3
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• Feedforward 3D Editing Learns from Semantic-Part Transformation
• Towards Scalable and Consistent 3D Editing

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