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BestWishYsh
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1 day ago
UniWorld: High-Resolution Semantic Encoders for Unified Visual
Understanding and Generation
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🌞 May 2025 - Open works from the Chinese community
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LanguageBind/UniWorld-V1
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replied to
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post
8 days ago
reacted to
AdinaY's
post with 🔥
8 days ago
Post
2623
🔥 New benchmark & dataset for Subject-to-Video generation
OPENS2V-NEXUS by Pekin University
✨ Fine-grained evaluation for subject consistency
BestWishYsh/OpenS2V-Eval
✨ 5M-scale dataset:
BestWishYsh/OpenS2V-5M
✨ New metrics – automatic scores for identity, realism, and text match
OPENS2V-NEXUS by Pekin University
✨ Fine-grained evaluation for subject consistency
BestWishYsh/OpenS2V-Eval
✨ 5M-scale dataset:
BestWishYsh/OpenS2V-5M
✨ New metrics – automatic scores for identity, realism, and text match
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- 2 replies
Thanks for sharing!
reacted to
AdinaY's
post with ❤️
8 days ago
Post
2623
🔥 New benchmark & dataset for Subject-to-Video generation
OPENS2V-NEXUS by Pekin University
✨ Fine-grained evaluation for subject consistency
BestWishYsh/OpenS2V-Eval
✨ 5M-scale dataset:
BestWishYsh/OpenS2V-5M
✨ New metrics – automatic scores for identity, realism, and text match
OPENS2V-NEXUS by Pekin University
✨ Fine-grained evaluation for subject consistency
BestWishYsh/OpenS2V-Eval
✨ 5M-scale dataset:
BestWishYsh/OpenS2V-5M
✨ New metrics – automatic scores for identity, realism, and text match
-
-
- 2 replies
reacted to
their
post with 🚀🔥
9 days ago
Post
2552
Introducing our new work: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation 🚀
We tackle the core challenges of Subject-to-Video Generation (S2V) by systematically building the first complete infrastructure—featuring an evaluation benchmark and a million-scale dataset! ✨
🧠 Introducing OpenS2V-Eval—the first fine-grained S2V benchmark, with 180 multi-domain prompts + real/synthetic test pairs. We propose NexusScore, NaturalScore, and GmeScore to precisely quantify model performance across subject consistency, naturalness, and text alignment ✔
📊 Using this framework, we conduct a comprehensive evaluation of 16 leading S2V models, revealing their strengths/weaknesses in complex scenarios!
🔥 OpenS2V-5M dataset now available! A 5.4M 720P HD collection of subject-text-video triplets, enabled by cross-video association segmentation + multi-view synthesis for diverse subjects & high-quality annotations 🚀
All resources open-sourced: Paper, Code, Data, and Evaluation Tools 📄
Let's advance S2V research together! 💡
🔗 Links:
Paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation (2505.20292)
Code: https://github.com/PKU-YuanGroup/OpenS2V-Nexus
Project: https://pku-yuangroup.github.io/OpenS2V-Nexus
LeaderBoard: BestWishYsh/OpenS2V-Eval
OpenS2V-Eval: BestWishYsh/OpenS2V-Eval
OpenS2V-5M: BestWishYsh/OpenS2V-5M
We tackle the core challenges of Subject-to-Video Generation (S2V) by systematically building the first complete infrastructure—featuring an evaluation benchmark and a million-scale dataset! ✨
🧠 Introducing OpenS2V-Eval—the first fine-grained S2V benchmark, with 180 multi-domain prompts + real/synthetic test pairs. We propose NexusScore, NaturalScore, and GmeScore to precisely quantify model performance across subject consistency, naturalness, and text alignment ✔
📊 Using this framework, we conduct a comprehensive evaluation of 16 leading S2V models, revealing their strengths/weaknesses in complex scenarios!
🔥 OpenS2V-5M dataset now available! A 5.4M 720P HD collection of subject-text-video triplets, enabled by cross-video association segmentation + multi-view synthesis for diverse subjects & high-quality annotations 🚀
All resources open-sourced: Paper, Code, Data, and Evaluation Tools 📄
Let's advance S2V research together! 💡
🔗 Links:
Paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation (2505.20292)
Code: https://github.com/PKU-YuanGroup/OpenS2V-Nexus
Project: https://pku-yuangroup.github.io/OpenS2V-Nexus
LeaderBoard: BestWishYsh/OpenS2V-Eval
OpenS2V-Eval: BestWishYsh/OpenS2V-Eval
OpenS2V-5M: BestWishYsh/OpenS2V-5M
-
- 1 reply
posted
an
update
9 days ago
Post
2552
Introducing our new work: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation 🚀
We tackle the core challenges of Subject-to-Video Generation (S2V) by systematically building the first complete infrastructure—featuring an evaluation benchmark and a million-scale dataset! ✨
🧠 Introducing OpenS2V-Eval—the first fine-grained S2V benchmark, with 180 multi-domain prompts + real/synthetic test pairs. We propose NexusScore, NaturalScore, and GmeScore to precisely quantify model performance across subject consistency, naturalness, and text alignment ✔
📊 Using this framework, we conduct a comprehensive evaluation of 16 leading S2V models, revealing their strengths/weaknesses in complex scenarios!
🔥 OpenS2V-5M dataset now available! A 5.4M 720P HD collection of subject-text-video triplets, enabled by cross-video association segmentation + multi-view synthesis for diverse subjects & high-quality annotations 🚀
All resources open-sourced: Paper, Code, Data, and Evaluation Tools 📄
Let's advance S2V research together! 💡
🔗 Links:
Paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation (2505.20292)
Code: https://github.com/PKU-YuanGroup/OpenS2V-Nexus
Project: https://pku-yuangroup.github.io/OpenS2V-Nexus
LeaderBoard: BestWishYsh/OpenS2V-Eval
OpenS2V-Eval: BestWishYsh/OpenS2V-Eval
OpenS2V-5M: BestWishYsh/OpenS2V-5M
We tackle the core challenges of Subject-to-Video Generation (S2V) by systematically building the first complete infrastructure—featuring an evaluation benchmark and a million-scale dataset! ✨
🧠 Introducing OpenS2V-Eval—the first fine-grained S2V benchmark, with 180 multi-domain prompts + real/synthetic test pairs. We propose NexusScore, NaturalScore, and GmeScore to precisely quantify model performance across subject consistency, naturalness, and text alignment ✔
📊 Using this framework, we conduct a comprehensive evaluation of 16 leading S2V models, revealing their strengths/weaknesses in complex scenarios!
🔥 OpenS2V-5M dataset now available! A 5.4M 720P HD collection of subject-text-video triplets, enabled by cross-video association segmentation + multi-view synthesis for diverse subjects & high-quality annotations 🚀
All resources open-sourced: Paper, Code, Data, and Evaluation Tools 📄
Let's advance S2V research together! 💡
🔗 Links:
Paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation (2505.20292)
Code: https://github.com/PKU-YuanGroup/OpenS2V-Nexus
Project: https://pku-yuangroup.github.io/OpenS2V-Nexus
LeaderBoard: BestWishYsh/OpenS2V-Eval
OpenS2V-Eval: BestWishYsh/OpenS2V-Eval
OpenS2V-5M: BestWishYsh/OpenS2V-5M
-
- 1 reply
reacted to
their
post with 👀🔥
2 months ago
Post
2850
🚨 Hot Take: GPT-4o might NOT be a purely autoregressive model! 🚨
There’s a high chance it has a diffusion head. 🤯 If true, this could be a game-changer for AI architecture. What do you think? 🤔👇
Code: https://github.com/PicoTrex/GPT-ImgEval
Dataset: Yejy53/GPT-ImgEval
Paper: GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation (2504.02782)
There’s a high chance it has a diffusion head. 🤯 If true, this could be a game-changer for AI architecture. What do you think? 🤔👇
Code: https://github.com/PicoTrex/GPT-ImgEval
Dataset: Yejy53/GPT-ImgEval
Paper: GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation (2504.02782)
posted
an
update
2 months ago
Post
2850
🚨 Hot Take: GPT-4o might NOT be a purely autoregressive model! 🚨
There’s a high chance it has a diffusion head. 🤯 If true, this could be a game-changer for AI architecture. What do you think? 🤔👇
Code: https://github.com/PicoTrex/GPT-ImgEval
Dataset: Yejy53/GPT-ImgEval
Paper: GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation (2504.02782)
There’s a high chance it has a diffusion head. 🤯 If true, this could be a game-changer for AI architecture. What do you think? 🤔👇
Code: https://github.com/PicoTrex/GPT-ImgEval
Dataset: Yejy53/GPT-ImgEval
Paper: GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation (2504.02782)