{"total":35,"rows":[{"id":"2510.07316","title":"Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers","created":"2025-10-08","cited_by_count":51,"area":"diffusion"},{"id":"2504.11427","title":"NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors","created":"2025-04-15","cited_by_count":25,"area":"diffusion"},{"id":"2509.18096","title":"Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers","created":"2025-09-22","cited_by_count":24,"area":"diffusion"},{"id":"2503.15905","title":"Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation","created":"2025-03-20","cited_by_count":21,"area":"diffusion"},{"id":"2405.20282","title":"SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow","created":"2024-05-30","cited_by_count":20,"area":"diffusion"},{"id":"2412.09593","title":"Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusion","created":"2024-12-12","cited_by_count":19,"area":"diffusion"},{"id":"2501.02576","title":"DepthMaster: Taming Diffusion Models for Monocular Depth Estimation","created":"2025-01-05","cited_by_count":19,"area":"diffusion"},{"id":"2508.14461","title":"Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering","created":"2025-08-20","cited_by_count":16,"area":"diffusion"},{"id":"2509.03680","title":"LuxDiT: Lighting Estimation with Video Diffusion Transformer","created":"2025-09-03","cited_by_count":15,"area":"diffusion"},{"id":"2503.17347","title":"Dereflection Any Image with Diffusion Priors and Diversified Data","created":"2025-03-21","cited_by_count":12,"area":"diffusion"},{"id":"2405.15619","title":"DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation","created":"2024-05-24","cited_by_count":9,"area":"diffusion"},{"id":"2505.24521","title":"UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation","created":"2025-05-30","cited_by_count":9,"area":"diffusion"},{"id":"2506.07280","title":"From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models","created":"2025-06-08","cited_by_count":8,"area":"diffusion"},{"id":"2503.15877","title":"Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation","created":"2025-03-20","cited_by_count":7,"area":"diffusion"},{"id":"2511.21129","title":"CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion","created":"2025-11-26","cited_by_count":6,"area":"diffusion"},{"id":"2511.18673","title":"Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers","created":"2025-11-24","cited_by_count":5,"area":"diffusion"},{"id":"2603.25892","title":"THFM: A Unified Video Foundation Model for 4D Human Perception and Beyond","created":"2026-03-26","cited_by_count":5,"area":"diffusion"},{"id":"2508.00443","title":"SDMatte: Grafting Diffusion Models for Interactive Matting","created":"2025-08-01","cited_by_count":4,"area":"diffusion"},{"id":"2509.24980","title":"SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimation","created":"2025-09-29","cited_by_count":3,"area":"diffusion"},{"id":"2605.00658","title":"UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors","created":"2026-05-01","cited_by_count":3,"area":"diffusion"},{"id":"2506.08013","title":"StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets","created":"2025-06-09","cited_by_count":2,"area":"diffusion"},{"id":"2508.19789","title":"StableIntrinsic: Detail-preserving One-step Diffusion Model for Multi-view Material Estimation","created":"2025-08-27","cited_by_count":2,"area":"diffusion"},{"id":"2512.13991","title":"Repurposing 2D Diffusion Models for 3D Shape Completion","created":"2025-12-16","cited_by_count":1,"area":"diffusion"},{"id":"2603.16340","title":"Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation","created":"2026-03-17","cited_by_count":1,"area":"diffusion"},{"id":"2609.08084","title":"Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation","created":"2026-09-08","cited_by_count":1,"area":"diffusion"},{"id":"2510.15510","title":"Exploring Conditions for Diffusion models in Robotic Control","created":"2025-10-17","cited_by_count":0,"area":"diffusion"},{"id":"2512.07062","title":"$\\mathrm{D}^\\mathrm{3}$-Predictor: Noise-Free Deterministic Diffusion for Dense Prediction","created":"2025-12-08","cited_by_count":0,"area":"diffusion"},{"id":"2603.19218","title":"Rethinking Vector Field Learning for Generative Segmentation","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.05077","title":"FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching","created":"2026-05-06","cited_by_count":0,"area":"diffusion"},{"id":"2606.16673","title":"MMDiff: Extending Diffusion Transformers for Multi-Modal Generation","created":"2026-06-15","cited_by_count":0,"area":"diffusion"},{"id":"2606.30370","title":"MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction","created":"2026-06-29","cited_by_count":0,"area":"diffusion"},{"id":"2607.12433","title":"ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning","created":"2026-07-14","cited_by_count":0,"area":"diffusion"},{"id":"2609.06665","title":"TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation","created":"2026-09-06","cited_by_count":0,"area":"diffusion"},{"id":"2609.28339","title":"Beyond Future Prediction: Denoising as Generative Adaptation for Robot Control","created":"2026-09-23","cited_by_count":0,"area":"diffusion"},{"id":"2610.00483","title":"PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion","created":"2026-09-30","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}