https://github.com/0zgur0/Seq_Scene_Gen
공식 코드A Layer-Based Sequential Framework for Scene Generation with GANs
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/3dlg-hcvc/NuiScene
공식 코드NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
https://github.com/Ariostgx/lctgen
공식 코드https://github.com/BerkeleyLearnVerify/Scenic
공식 코드Scenic: A Language for Scenario Specification and Scene Generation
https://github.com/Ha0Tang/LGGAN
공식 코드https://github.com/Lakonik/SSDNeRF
공식 코드Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction
https://github.com/Papple-F/csg
공식 코드Training-free Composite Scene Generation for Layout-to-Image Synthesis
https://github.com/Pokerlishao/LoopGaussian
공식 코드LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
https://github.com/SSEditor/SSEditor
공식 코드SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model
https://github.com/SparklingH/BloomScene
공식 코드BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation
https://github.com/Tencent/BlockFusion
공식 코드BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
https://github.com/VincentHancoder/REPARO
공식 코드REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment
https://github.com/Yuheng-SWJTU/pyramid-discrete-diffusion
공식 코드https://github.com/aimagelab/alfie
공식 코드https://github.com/akirahero/diffindscene
공식 코드DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation
https://github.com/alexj94/future_urban_scene_generation
공식 코드https://github.com/aluo-x/3D_SLN
공식 코드https://github.com/amazon-research/indoor-scene-generation-eai
공식 코드LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
https://github.com/angeladai/spsg
공식 코드SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
https://github.com/anshgs/earthgen
공식 코드https://github.com/apple/ml-gaudi
공식 코드https://github.com/apple/ml-gsn
공식 코드Unconstrained Scene Generation with Locally Conditioned Radiance Fields
https://github.com/applied-ai-lab/genesis
공식 코드GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
https://github.com/ashual/scene_generation
공식 코드Specifying Object Attributes and Relations in Interactive Scene Generation
https://github.com/autonomousvision/graf
공식 코드GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
https://github.com/azadis/SB-GAN
공식 코드https://github.com/byeongjun-park/SteerX
공식 코드SteerX: Creating Any Camera-Free 3D and 4D Scenes with Geometric Steering
https://github.com/bytedance/xverse
공식 코드XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation
https://github.com/chaos-sun/t3s2s
공식 코드T$^3$-S2S: Training-free Triplet Tuning for Sketch to Scene Generation
https://github.com/codegoat24/face-diffuser
공식 코드https://github.com/continental/ep-diffuser
공식 코드https://github.com/cy94/sceneformer
공식 코드https://github.com/dk-liang/unifuture
공식 코드https://github.com/dorarad/gansformer
공식 코드https://github.com/dreamscene4d/dreamscene4d
공식 코드DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
https://github.com/dw-dengwei/treesearchgen
공식 코드Global-Local Tree Search in VLMs for 3D Indoor Scene Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.