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Advances in 3D Generation: A Survey
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Advances in 3D Generation: A Survey
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Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.
Forward citations
Cited by 16 Pith papers
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IDEAL-Bench: Indoor Dataset and Evaluation suite for Analyzing 3D Layout reasoning
Current VLMs top out at 62.1/100 on holistic single-image 3D indoor layout prediction, with strong recognition but weak geometric regression, and mid-tier rankings that shift relative to QA and primitive-reconstructio...
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GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
GTA generates 3D worlds from single images via a two-stage video diffusion process that prioritizes geometry before appearance to improve structural consistency.
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3D Generation for Embodied AI and Robotic Simulation: A Survey
3D generation for embodied AI is shifting from visual realism toward interaction readiness, organized into data generation, simulation environments, and sim-to-real bridging roles.
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Benchmarking Vision-Language Models under Contradictory Virtual Content Attacks in Augmented Reality
ContrAR benchmark reveals that current VLMs show reasonable understanding of contradictory virtual content in AR but need improvement in detection, reasoning, and balancing accuracy with latency.
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How to Spin an Object: First, Get the Shape Right
Camera-Relative Object Coordinates (CROCS) as an intermediate geometry representation in two-stage image-to-3D models yields superior novel-view quality, geometric accuracy, and multiview consistency over depth maps, ...
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Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.
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FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
FoundObj uses foundation-model priors as RL rewards to discover multi-class 3D objects from point clouds without scene-level labels.
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Perceive-then-Plan: Layout-as-Policy for Monocular 3D Scene Layout Estimation
Introduces Layout-as-Policy (LaP) to turn 3D layout estimation into an iterative policy-learning refinement process for better physical coherence.
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Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion
DiLAST optimizes 3D latents via guidance from a 2D diffusion model to enable generalizable style transfer for OOD styles in 3D asset generation.
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Embody4D: A Generalist Data Engine for Embodied 4D World Modeling
Embody4D generates novel-view videos from monocular robot videos via a 3D-aware synthesis pipeline, confidence-aware expert modulation, and interaction-aware attention for embodied 4D world modeling.
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Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D mitigates 3D/4D generation hallucinations via LMM-based detection, multi-model voting correction, and motion-aware optimization without retraining base generators.
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Embody4D: A Generalist Data Engine for Embodied 4D World Modeling
Embody4D generates high-fidelity, view-consistent novel views from monocular videos for embodied scenarios via 3D-aware data synthesis, adaptive noise injection, and interaction-aware attention.
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SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training
A test-time-trained feedforward model that propagates 2D edits onto 3D Gaussian attributes at interactive speeds.
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TripoSR: Fast 3D Object Reconstruction from a Single Image
TripoSR generates 3D meshes from single images in under 0.5 seconds using an improved transformer architecture over LRM.
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3D Generation for Embodied AI and Robotic Simulation: A Survey
The survey organizes 3D generation for embodied AI into data generators for assets, simulation environments for interaction, and sim-to-real bridges, noting a shift toward interaction readiness and listing bottlenecks...
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3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and...
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