https://github.com/nv-tlabs/XCube
공식 코드XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
Papers with Code 아카이브
Scene Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/oaishi/3DScene_from_text
공식 코드Static and Animated 3D Scene Generation from Free-form Text Descriptions
https://github.com/orangesodahub/infgen
공식 코드Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation
https://github.com/orangesodahub/scenecraft
공식 코드https://github.com/pbonazzi/graph-scene-transformer
공식 코드https://github.com/pemami4911/segregate-relate-imagine
공식 코드Towards Improving the Generation Quality of Autoregressive Slot VAEs
https://github.com/pku-yuangroup/holotime
공식 코드HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
https://github.com/pzyseere/metaspatial
공식 코드MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
https://github.com/rishidarkdevil/daam-i2i
공식 코드https://github.com/roblox/cube
공식 코드https://github.com/roeiherz/CanonicalSg2Im
공식 코드Learning Canonical Representations for Scene Graph to Image Generation
https://github.com/scope-lab-vu/risk-aware-scene-generation-cps
공식 코드Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems
https://github.com/thudm/scenegenagent
공식 코드SceneGenAgent: Precise Industrial Scene Generation with Coding Agent
https://github.com/tqtqliu/free4d
공식 코드Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
https://github.com/valeoai/SemanticPalette
공식 코드Semantic Palette: Guiding Scene Generation with Class Proportions
https://github.com/wzzheng/gpd
공식 코드https://github.com/xingyi-li/icontrol3d
공식 코드iControl3D: An Interactive System for Controllable 3D Scene Generation
https://github.com/yangchen1997/wcdt
공식 코드WcDT: World-centric Diffusion Transformer for Traffic Scene Generation
https://github.com/yanghtr/ssgnet
공식 코드LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
https://github.com/yangzhaojason/DualDiff
공식 코드DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance
https://github.com/yangzhifeio/MMGDreamer
공식 코드MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation
https://github.com/ymxlzgy/commonscenes
공식 코드CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
https://github.com/yuhengliu02/control-3d-scene
공식 코드https://github.com/yuhengliu02/pyramid-discrete-diffusion
공식 코드https://github.com/yunishi3/3D-FCR-alphaGAN
공식 코드Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes
https://github.com/yzhou359/3DIndoor-SceneGraphNet
공식 코드SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
https://github.com/zhao-yiqun/roomdesigner
공식 코드https://github.com/zihao-ai/eairiskbench
공식 코드https://github.com/zihao-ai/earbench
공식 코드https://github.com/zju3dv/diffpano
공식 코드https://github.com/zoomin-lee/semcity
공식 코드https://github.com/zyp123494/dynavol
공식 코드Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
https://gitlab.com/europeanspaceagency/trajectory-to-events
공식 코드On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing
https://github.com/3dtopia/dynamiccity
DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes
https://github.com/AkiraTOSEI/Funnel-Activation-for-Visual-Recognition
https://github.com/Godofnothing/pi-GAN
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.