content.raw: 7 Open-source Methods to Improve Video Generation and Understanding
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content.value: 7 Open-source Methods to Improve Video Generation and Understanding
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content.raw: AI community is making great strides toward achieving the full potential of multimodality in video generation and understanding.  Last week studies showed that working with videos is now one of the main focuses for improving AI models. Another highlight of the week is that open source, once again, proves its value. For those who were impressed by DeepSeek-R1, we’re with you!
content.type: text
content.value: AI community is making great strides toward achieving the full potential of multimodality in video generation and understanding.  Last week studies showed that working with videos is now one of the main focuses for improving AI models. Another highlight of the week is that open source, once again, proves its value. For those who were impressed by DeepSeek-R1, we’re with you!
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content.raw: Today, we’re combining these two key focuses and bringing you a list of open-source methods for better video generation and understanding:
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content.value: Today, we’re combining these two key focuses and bringing you a list of open-source methods for better video generation and understanding:
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content.raw: 1. VideoLLaMA 3 model: Excels in various video and image tasks thanks to vision-centric training approach. 
content.type: text
content.value: 1. VideoLLaMA 3 model: Excels in various video and image tasks thanks to vision-centric training approach. 
content.label: VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video
    Understanding (2501.13106)
content.raw: https://huggingface.co/papers/2501.13106
content.id: 2501.13106
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.13106
content.type: new_line
content.type: new_line
content.raw: 2. FILMAGENT framework assigns roles to multiple AI agents, like a director, screenwriter, actor, and cinematographer, to automate the filmmaking process in 3D virtual environments. 
content.type: text
content.value: 2. FILMAGENT framework assigns roles to multiple AI agents, like a director, screenwriter, actor, and cinematographer, to automate the filmmaking process in 3D virtual environments. 
content.label: FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in
    Virtual 3D Spaces (2501.12909)
content.raw: https://huggingface.co/papers/2501.12909
content.id: 2501.12909
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.12909
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content.type: new_line
content.raw: 3. 
content.type: text
content.value: 3. 
content.label: Improving Video Generation with Human Feedback (2501.13918)
content.raw: https://huggingface.co/papers/2501.13918
content.id: 2501.13918
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.13918
content.raw:  proposes a new VideoReward Model and approach that uses human feedback to refine video generation models.
content.type: text
content.value:  proposes a new VideoReward Model and approach that uses human feedback to refine video generation models.
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content.raw: 4. DiffuEraser video inpainting model, based on stable diffusion, is designed to fill in missing areas with detailed, realistic content and to ensure consistent structures across frames. 
content.type: text
content.value: 4. DiffuEraser video inpainting model, based on stable diffusion, is designed to fill in missing areas with detailed, realistic content and to ensure consistent structures across frames. 
content.label: DiffuEraser: A Diffusion Model for Video Inpainting (2501.10018)
content.raw: https://huggingface.co/papers/2501.10018
content.id: 2501.10018
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.10018
content.type: new_line
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content.raw: 5. MAGI is a hybrid video gen model that combines masked and casual modeling. Its key innovation, Complete Teacher Forcing (CTF), conditions masked frames on fully visible frames. 
content.type: text
content.value: 5. MAGI is a hybrid video gen model that combines masked and casual modeling. Its key innovation, Complete Teacher Forcing (CTF), conditions masked frames on fully visible frames. 
content.label: Taming Teacher Forcing for Masked Autoregressive Video Generation (2501.12389)
content.raw: https://huggingface.co/papers/2501.12389
content.id: 2501.12389
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.12389
content.type: new_line
content.type: new_line
content.raw: 6. 
content.type: text
content.value: 6. 
content.label: Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using
    Real-Time Warped Noise (2501.08331)
content.raw: https://huggingface.co/papers/2501.08331
content.id: 2501.08331
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.08331
content.raw:  proposes  motion control, allowing users to guide how objects or the camera move in generated videos. Its noise warping algorithm replaces random noise in videos with structured noise based on motion info.
content.type: text
content.value:  proposes  motion control, allowing users to guide how objects or the camera move in generated videos. Its noise warping algorithm replaces random noise in videos with structured noise based on motion info.
content.type: new_line
content.type: new_line
content.raw: 7. Video Depth Anything model estimates depth consistently in super-long videos (several minutes or more) without sacrificing quality or speed. 
content.type: text
content.value: 7. Video Depth Anything model estimates depth consistently in super-long videos (several minutes or more) without sacrificing quality or speed. 
content.label: Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (2501.12375)
content.raw: https://huggingface.co/papers/2501.12375
content.id: 2501.12375
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2501.12375
rawContent: 7 Open-source Methods to Improve Video Generation and Understanding
  
  AI community is making great strides toward achieving the full potential of multimodality in video generation and understanding.  Last week studies showed that working with videos is now one of the main focuses for improving AI models. Another highlight of the week is that open source, once again, proves its value. For those who were impressed by DeepSeek-R1, we’re with you!
  
  Today, we’re combining these two key focuses and bringing you a list of open-source methods for better video generation and understanding:
  
  1. VideoLLaMA 3 model: Excels in various video and image tasks thanks to vision-centric training approach. https://huggingface.co/papers/2501.13106
  
  2. FILMAGENT framework assigns roles to multiple AI agents, like a director, screenwriter, actor, and cinematographer, to automate the filmmaking process in 3D virtual environments. https://huggingface.co/papers/2501.12909
  
  3. https://huggingface.co/papers/2501.13918 proposes a new VideoReward Model and approach that uses human feedback to refine video generation models.
  
  4. DiffuEraser video inpainting model, based on stable diffusion, is designed to fill in missing areas with detailed, realistic content and to ensure consistent structures across frames. https://huggingface.co/papers/2501.10018
  
  5. MAGI is a hybrid video gen model that combines masked and casual modeling. Its key innovation, Complete Teacher Forcing (CTF), conditions masked frames on fully visible frames. https://huggingface.co/papers/2501.12389
  
  6. https://huggingface.co/papers/2501.08331 proposes  motion control, allowing users to guide how objects or the camera move in generated videos. Its noise warping algorithm replaces random noise in videos with structured noise based on motion info.
  
  7. Video Depth Anything model estimates depth consistently in super-long videos (several minutes or more) without sacrificing quality or speed. https://huggingface.co/papers/2501.12375
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