AIMold: An Autonomous AI-based Pipeline for Complex Mold Design

arXiv:2608.00800 · cs.CV · Submitted 2026-08-01 · Read on arXiv

cs.CV

Submitted: 2026-08-01

Updated: 2026-09-16

Project page: https://tb2-sy.github.io/aimold

Terminology

• Diffusion Model — 8× in this paper · explained in 8 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• ACM Transactions on Graphics — 6× in this paper
• Point Clouds — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Latent Representation — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 7 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Chamfer Distance — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Jensen–Shannon Divergence (JSD) — 1× in this paper · explained in 2 episodes
• Minimum Matching Distance — 1× in this paper
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Score Distillation Sampling — 1× in this paper
• Tokenization — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
• SolidGen: An Autoregressive Model for Direct B-rep Synthesis
• LATTICE: Democratize High-Fidelity 3D Generation at Scale
• Flow Matching for Generative Modeling
• MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model
• B-repLer: Language-guided Editing of CAD Models
• Decoupled Weight Decay Regularization
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts
• Vitruvion: A Generative Model of Parametric CAD Sketches
• 3D generation on ImageNet
• Cube: A Roblox View of 3D Intelligence
• Native and Compact Structured Latents for 3D Generation
• CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM
• Hierarchical Neural Coding for Controllable CAD Model Generation
• SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
• FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models
• Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

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