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Advances in 4D Generation: A Survey
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Advances in 4D Generation: A Survey
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Generative artificial intelligence has recently progressed from static image and video synthesis to 3D content generation, culminating in the emergence of 4D generation-the task of synthesizing temporally coherent dynamic 3D assets guided by user input. As a burgeoning research frontier, 4D generation enables richer interactive and immersive experiences, with applications ranging from digital humans to autonomous driving. Despite rapid progress, the field lacks a unified understanding of 4D representations, generative frameworks, basic paradigms, and the core technical challenges it faces. This survey provides a systematic and in-depth review of the 4D generation landscape. To comprehensively characterize 4D generation, we first categorize fundamental 4D representations and outline associated techniques for 4D generation. We then present an in-depth analysis of representative generative pipelines based on conditions and representation methods. Subsequently, we discuss how motion and geometry priors are integrated into 4D outputs to ensure spatio-temporal consistency under various control schemes. From an application perspective, this paper summarizes 4D generation tasks in areas such as dynamic object/scene generation, digital human synthesis, editable 4D content, and embodied AI. Furthermore, we summarize and multi-dimensionally compare four basic paradigms for 4D generation: End-to-End, Generated-Data-Based, Implicit-Distillation-Based, and Explicit-Supervision-Based. Concluding our analysis, we highlight five key challenges-consistency, controllability, diversity, efficiency, and fidelity-and contextualize these with current approaches.By distilling recent advances and outlining open problems, this work offers a comprehensive and forward-looking perspective to guide future research in 4D generation.
Forward citations
Cited by 9 Pith papers
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One Video, One World: Turning Monocular Video into Physical 4D Scenes
OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from monocular video via a four-stage training-free pipeline and introduces a new benchmark for structured Video-to-4D evaluation.
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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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Alignment Is All You Need For X-to-4D Generation
Align4D introduces object distance alignment, motion-geometry joint alignment, asynchronous optimization, and the X4D dataset to achieve state-of-the-art X-to-4D generation from multimodal inputs.
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Geometric 4D Stitching for Grounded 4D Generation
Geometric 4D Stitching explicitly complements missing geometric regions in 4D generated scenes with grounded stitches to achieve consistent 4D representations in under 10 minutes on a single GPU.
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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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Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation
A diffusion model generates dense 4D point trajectories from a single image, and a separate view-synthesis module renders them into novel-view videos.
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Advances in 4D Representation: Geometry, Motion, and Interaction
A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.
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