MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Paper • 2604.08516 • Published • 41
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yyupenn
authored a
paper 5 days ago
yyupenn
authored 5
papers 11 days ago
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and Objects
Paper • 2410.02730 • Published
HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing
Paper • 2508.05899 • Published • 1
Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Paper • 2601.10611 • Published • 34
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Paper • 2603.18004 • Published • 13
MolmoPoint: Better Pointing for VLMs with Grounding Tokens
Paper • 2603.28069 • Published • 8
g-luo
authored a
paper 2 months ago
g-luo
submitted a
paper to Daily Papers 2 months ago
Post
882
Introducing StereoSpace -- our new end-to-end method for turning photos into stereo images without explicit geometry or depth maps. This makes it especially robust with thin structures and transparencies. Try the demo below:
🌐 Project: https://hf.co/spaces/prs-eth/stereospace_web
📕 Paper: StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space (2512.10959)
🐙 Code: https://github.com/prs-eth/stereospace
🤗 Demo: toshas/stereospace
🤗 Weights: prs-eth/stereospace-v1-0
By ETH Zürich (@behretj , @Bingxin , @konradschindler ), University of Bologna (@fabiotosi92 , @mpoggi ), HUAWEI Bayer Lab (@toshas ).
🌐 Project: https://hf.co/spaces/prs-eth/stereospace_web
📕 Paper: StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space (2512.10959)
🐙 Code: https://github.com/prs-eth/stereospace
🤗 Demo: toshas/stereospace
🤗 Weights: prs-eth/stereospace-v1-0
By ETH Zürich (@behretj , @Bingxin , @konradschindler ), University of Bologna (@fabiotosi92 , @mpoggi ), HUAWEI Bayer Lab (@toshas ).
toshas
authored a
paper 4 months ago
toshas
submitted a
paper to Daily Papers 4 months ago
Post
2317
Introducing 🇨🇭WindowSeat🇨🇭 –– our new method for removing reflections from photos taken through windows, on planes, in malls, offices, and other glass-filled environments.
Finetuning a foundation diffusion transformer for reflection removal quickly runs up against the limits of what existing datasets and techniques can offer. To fill that gap, we generate physically accurate examples in Blender that simulate realistic glass and reflection effects. This data enables strong performance on both established benchmarks and previously unseen images.
To make this practical, the open-source Apache-2 model builds on Qwen-Image-Edit-2509, a 20B image-editing diffusion transformer that runs on a single GPU and can be fine-tuned in about a day. WindowSeat keeps its use of the underlying DiT cleanly separated from the data and training recipe, allowing future advances in base models to be incorporated with minimal friction.
Try it out with your own photos in this interactive demo:
🤗 toshas/windowseat-reflection-removal
Other resources:
🌎 Website: huawei-bayerlab/windowseat-reflection-removal-web
🎓 Paper: Reflection Removal through Efficient Adaptation of Diffusion Transformers (2512.05000)
🤗 Model: huawei-bayerlab/windowseat-reflection-removal-v1-0
🐙 Code: https://github.com/huawei-bayerlab/windowseat-reflection-removal
Team: Daniyar Zakarin (@daniyarzt )*, Thiemo Wandel (@thiemo-wandel )*, Anton Obukhov (@toshas ), Dengxin Dai.
*Work done during internships at HUAWEI Bayer Lab
Finetuning a foundation diffusion transformer for reflection removal quickly runs up against the limits of what existing datasets and techniques can offer. To fill that gap, we generate physically accurate examples in Blender that simulate realistic glass and reflection effects. This data enables strong performance on both established benchmarks and previously unseen images.
To make this practical, the open-source Apache-2 model builds on Qwen-Image-Edit-2509, a 20B image-editing diffusion transformer that runs on a single GPU and can be fine-tuned in about a day. WindowSeat keeps its use of the underlying DiT cleanly separated from the data and training recipe, allowing future advances in base models to be incorporated with minimal friction.
Try it out with your own photos in this interactive demo:
🤗 toshas/windowseat-reflection-removal
Other resources:
🌎 Website: huawei-bayerlab/windowseat-reflection-removal-web
🎓 Paper: Reflection Removal through Efficient Adaptation of Diffusion Transformers (2512.05000)
🤗 Model: huawei-bayerlab/windowseat-reflection-removal-v1-0
🐙 Code: https://github.com/huawei-bayerlab/windowseat-reflection-removal
Team: Daniyar Zakarin (@daniyarzt )*, Thiemo Wandel (@thiemo-wandel )*, Anton Obukhov (@toshas ), Dengxin Dai.
*Work done during internships at HUAWEI Bayer Lab
toshas
authored 2
papers 4 months ago
yossig
authored a
paper 4 months ago
multimodalart
posted an update 6 months ago
Post
25170
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
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mbrack
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ermonste
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