3D Scene Generation: A Survey

3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on procedural rules offered scalability but limited diversity. Recent advances in deep generative models (e.g., GANs, diffusion models) and 3D representations (e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene distributions, improving fidelity, diversity, and view consistency. Recent advances like diffusion models bridge 3D scene synthesis and photorealism by reframing generation as image or video synthesis problems. This survey provides a systematic overview of state-of-the-art approaches, organizing them into four paradigms: procedural generation, neural 3D-based generation, image-based generation, and video-based generation. We analyze their technical foundations, trade-offs, and representative results, and review commonly used datasets, evaluation protocols, and downstream applications. We conclude by discussing key challenges in generation capacity, 3D representation, data and annotations, and evaluation, and outline promising directions including higher fidelity, physics-aware and interactive generation, and unified perception-generation models. This review organizes recent advances in 3D scene generation and highlights promising directions at the intersection of generative AI, 3D vision, and embodied intelligence. To track ongoing developments, we maintain an up-to-date project page: https://github.com/hzxie/Awesome-3D-Scene-Generation.

Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency

We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multiview videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.

Fusion of Millimeter-wave Radar and Pulse Oximeter Data for Low-burden Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome

Objective: The aim of the study is to develop a novel method for improved diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical or home settings, with the focus on achieving diagnostic performance comparable to the gold-standard polysomnography (PSG) with significantly reduced monitoring burden. Methods: We propose a method using millimeter-wave radar and pulse oximeter for OSAHS diagnosis (ROSA). It contains a sleep apnea-hypopnea events (SAE) detection network, which directly predicts the temporal localization of SAE, and a sleep staging network, which predicts the sleep stages throughout the night, based on radar signals. It also fuses oxygen saturation (SpO2) information from the pulse oximeter to adjust the score of SAE detected by radar. Results: Experimental results on a real-world dataset (>800 hours of overnight recordings, 100 subjects) demonstrated high agreement (ICC=0.9870) on apnea-hypopnea index (AHI) between ROSA and PSG. ROSA also exhibited excellent diagnostic performance, exceeding 90% in accuracy across AHI diagnostic thresholds of 5, 15 and 30 events/h. Conclusion: ROSA improves diagnostic accuracy by fusing millimeter-wave radar and pulse oximeter data. It provides a reliable and low-burden solution for OSAHS diagnosis. Significance: ROSA addresses the limitations of high complexity and monitoring burden associated with traditional PSG. The high accuracy and low burden of ROSA show its potential to improve the accessibility of OSAHS diagnosis among population.

CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities

3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like buildings and vehicles, and heightened human sensitivity to distortions in urban environments. To tackle these issues, we propose CityDreamer4D, a compositional generative model specifically tailored for generating unbounded 4D cities. Our main insights are 1) 4D city generation should separate dynamic objects (e.g., vehicles) from static scenes (e.g., buildings and roads), and 2) all objects in the 4D scene should be composed of different types of neural fields for buildings, vehicles, and background stuff. Specifically, we propose Traffic Scenario Generator and Unbounded Layout Generator to produce dynamic traffic scenarios and static city layouts using a highly compact BEV representation. Objects in 4D cities are generated by combining stuff-oriented and instance-oriented neural fields for background stuff, buildings, and vehicles. To suit the distinct characteristics of background stuff and instances, the neural fields employ customized generative hash grids and periodic positional embeddings as scene parameterizations. Furthermore, we offer a comprehensive suite of datasets for city generation, including OSM, GoogleEarth, and CityTopia. The OSM dataset provides a variety of real-world city layouts, while the Google Earth and CityTopia datasets deliver large-scale, high-quality city imagery complete with 3D instance annotations. Leveraging its compositional design, CityDreamer4D supports a range of downstream applications, such as instance editing, city stylization, and urban simulation, while delivering state-of-the-art performance in generating realistic 4D cities.

Brillouin photonics engine in the thin-film lithium niobate platform

Stimulated Brillouin scattering (SBS) is revolutionizing low-noise lasers and microwave photonic systems. However, despite extensive explorations of a low-loss and versatile integrated platform for Brillouin photonic circuits, current options fall short due to limited technological scalability or inadequate SBS gain. Here we introduce the thin-film lithium niobate (TFLN) platform as the go-to choice for integrated Brillouin photonics applications. We report the angle-dependent strong SBS gain in this platform, which can overcome the intrinsic propagation loss. Furthermore, we demonstrate the first stimulated Brillouin laser in TFLN with a tuning range > 20 nm and utilize it to achieve high-purity RF signal generation with an intrinsic linewidth of 9 Hz. Finally, we devise a high-rejection Brillouin-based microwave photonic notch filter, for the first time, integrating an SBS spiral, an on-chip modulator, and a tunable ring all within the same platform. This TFLN-based Brillouin photonics engine uniquely combines the scalability of this platform and the versatility of SBS. Moreover, it bridges SBS with other functionalities in the TFLN platform, unlocking new possibilities for Brillouin-based applications with unparalleled performances.

Deep Learning-based Automated Diagnosis of Obstructive Sleep Apnea and Sleep Stage Classification in Children Using Millimeter-wave Radar and Pulse Oximeter

Study Objectives: To evaluate the agreement between the millimeter-wave radar-based device and polysomnography (PSG) in diagnosis of obstructive sleep apnea (OSA) and classification of sleep stage in children. Methods: 281 children, aged 1 to 18 years, who underwent sleep monitoring between September and November 2023 at the Sleep Center of Beijing Children's Hospital, Capital Medical University, were recruited in the study. All enrolled children underwent sleep monitoring by PSG and the millimeter-wave radar-based device, QSA600, simultaneously. QSA600 recordings were automatically analyzed using a deep learning model meanwhile the PSG data was manually scored. Results: The Obstructive Apnea-Hypopnea Index (OAHI) obtained from QSA600 and PSG demonstrates a high level of agreement with an intraclass correlation coefficient of 0.945 (95% CI: 0.93 to 0.96). Bland-Altman analysis indicates that the mean difference of OAHI between QSA600 and PSG is -0.10 events/h (95% CI: -11.15 to 10.96). The deep learning model evaluated through cross-validation showed good sensitivity (81.8%, 84.3% and 89.7%) and specificity (90.5%, 95.3% and 97.1%) values for diagnosing children with OAHI>1, OAHI>5 and OAHI>10. The area under the receiver operating characteristic curve is 0.923, 0.955 and 0.988, respectively. For sleep stage classification, the model achieved Kappa coefficients of 0.854, 0.781, and 0.734, with corresponding overall accuracies of 95.0%, 84.8%, and 79.7% for Wake-sleep classification, Wake-REM-Light-Deep classification, and Wake-REM-N1-N2 N3 classification, respectively. Conclusions: QSA600 has demonstrated high agreement with PSG in diagnosing OSA and performing sleep staging in children. The device is portable, low-load and suitable for follow up and long-term pediatric sleep assessment.

Detection of Sleep Apnea-Hypopnea Events Using Millimeter-wave Radar and Pulse Oximeter

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep-related breathing disorder associated with significant morbidity and mortality worldwide. The gold standard for OSAHS diagnosis, polysomnography (PSG), faces challenges in popularization due to its high cost and complexity. Recently, radar has shown potential in detecting sleep apnea-hypopnea events (SAE) with the advantages of low cost and non-contact monitoring. However, existing studies, especially those using deep learning, employ segment-based classification approach for SAE detection, making the task of event quantity estimation difficult. Additionally, radar-based SAE detection is susceptible to interference from body movements and the environment. Oxygen saturation (SpO2) can offer valuable information about OSAHS, but it also has certain limitations and cannot be used alone for diagnosis. In this study, we propose a method using millimeter-wave radar and pulse oximeter to detect SAE, called ROSA. It fuses information from both sensors, and directly predicts the temporal localization of SAE. Experimental results demonstrate a high degree of consistency (ICC=0.9864) between AHI from ROSA and PSG. This study presents an effective method with low-load device for the diagnosis of OSAHS.

FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models

Diffusion model has demonstrated remarkable capability in video generation, which further sparks interest in introducing trajectory control into the generation process. While existing works mainly focus on training-based methods (e.g., conditional adapter), we argue that diffusion model itself allows decent control over the generated content without requiring any training. In this study, we introduce a tuning-free framework to achieve trajectory-controllable video generation, by imposing guidance on both noise construction and attention computation. Specifically, 1) we first show several instructive phenomenons and analyze how initial noises influence the motion trajectory of generated content. 2) Subsequently, we propose FreeTraj, a tuning-free approach that enables trajectory control by modifying noise sampling and attention mechanisms. 3) Furthermore, we extend FreeTraj to facilitate longer and larger video generation with controllable trajectories. Equipped with these designs, users have the flexibility to provide trajectories manually or opt for trajectories automatically generated by the LLM trajectory planner. Extensive experiments validate the efficacy of our approach in enhancing the trajectory controllability of video diffusion models.

GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation

3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation. However, adapting 3D-GS from finite-scale 3D objects and humans to infinite-scale 3D cities is non-trivial. Unbounded 3D city generation entails significant storage overhead (out-of-memory issues), arising from the need to expand points to billions, often demanding hundreds of Gigabytes of VRAM for a city scene spanning 10km^2. In this paper, we propose GaussianCity, a generative Gaussian Splatting framework dedicated to efficiently synthesizing unbounded 3D cities with a single feed-forward pass. Our key insights are two-fold: 1) Compact 3D Scene Representation: We introduce BEV-Point as a highly compact intermediate representation, ensuring that the growth in VRAM usage for unbounded scenes remains constant, thus enabling unbounded city generation. 2) Spatial-aware Gaussian Attribute Decoder: We present spatial-aware BEV-Point decoder to produce 3D Gaussian attributes, which leverages Point Serializer to integrate the structural and contextual characteristics of BEV points. Extensive experiments demonstrate that GaussianCity achieves state-of-the-art results in both drone-view and street-view 3D city generation. Notably, compared to CityDreamer, GaussianCity exhibits superior performance with a speedup of 60 times (10.72 FPS v.s. 0.18 FPS).

FashionEngine: Interactive Generation and Editing of 3D Clothed Humans

We present FashionEngine, an interactive 3D human generation and editing system that allows us to design 3D digital humans in a way that aligns with how humans interact with the world, such as natural languages, visual perceptions, and hand-drawing. FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, which provides strong priors for diverse generation and editing tasks. 2) Multimodality-UV Space encoding the texture appearance, shape topology, and textual semantics of human clothing in a canonical UV-aligned space, which faithfully aligns the user multimodal inputs with the implicit UV latent space for controllable 3D human editing. The multimodality-UV space is shared across different user inputs, such as texts, images, and sketches, which enables various joint multimodal editing tasks. 3) Multimodality-UV Aligned Sampler learns to sample high-quality and diverse 3D humans from the diffusion prior for multimodal user inputs. Extensive experiments validate FashionEngine's state-of-the-art performance for conditional generation/editing tasks. In addition, we present an interactive user interface for our FashionEngine that enables both conditional and unconditional generation tasks, and editing tasks including pose/view/shape control, text-, image-, and sketch-driven 3D human editing and 3D virtual try-on, in a unified framework. Our project page is at: https://taohuumd.github.io/projects/FashionEngine.