uva-cv-lab

5 models • 4 total models in database
Sort by:

FrameINO_Wan2.2_5B_Stage2_MotionINO_v1.5

Frame In-N-Out: Unbounded Controllable Image-to-Video Generation Frame In-N-Out is a controllable Image-to-Video generation Diffusion Transformer model where objects can enter or exit the scene along user-specified motion trajectories and ID reference. Our method introduces a new dataset curation pattern recognition, evaluation protocol, and a motion-controllable , identity-preserving , unbounded canvas Video Diffusion Transformer, to achieve Frame In and Frame Out in the cinematic domain. Model Zoo 🤗 | Model | Description | Huggingface | |--------------------------------------------------------------- | -------------------------------| ------------------------------------------------------------------------------------------------| | CogVideoX-I2V-5B V1.0 (Stage 1 - Motion Control) | Paper Weight v1.0 | Download | | CogVideoX-I2V-5B (Stage 2 - Motion + In-N-Out Control) | Paper Weight v1.0 | Download | | Wan2.2-TI2V-5B (Stage 1 - Motion Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.6 on Arbitrary Resolution | Download |

NaNK
license:gpl-3.0
173
3

FrameINO_Wan2.2_5B_Stage1_Motion_v1.5

Frame In-N-Out: Unbounded Controllable Image-to-Video Generation Frame In-N-Out is a controllable Image-to-Video generation Diffusion Transformer model where objects can enter or exit the scene along user-specified motion trajectories and ID reference. Our method introduces a new dataset curation pattern recognition, evaluation protocol, and a motion-controllable , identity-preserving , unbounded canvas Video Diffusion Transformer, to achieve Frame In and Frame Out in the cinematic domain. Model Zoo 🤗 | Model | Description | Huggingface | |--------------------------------------------------------------- | -------------------------------| ------------------------------------------------------------------------------------------------| | CogVideoX-I2V-5B V1.0 (Stage 1 - Motion Control) | Paper Weight v1.0 | Download | | CogVideoX-I2V-5B (Stage 2 - Motion + In-N-Out Control) | Paper Weight v1.0 | Download | | Wan2.2-TI2V-5B (Stage 1 - Motion Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.6 on Arbitrary Resolution | Download |

NaNK
license:gpl-3.0
138
3

FrameINO_CogVideoX_Stage2_MotionINO_v1.0

Frame In-N-Out: Unbounded Controllable Image-to-Video Generation Frame In-N-Out is a controllable Image-to-Video generation Diffusion Transformer model where objects can enter or exit the scene along user-specified motion trajectories and ID reference. Our method introduces a new dataset curation pattern recognition, evaluation protocol, and a motion-controllable , identity-preserving , unbounded canvas Video Diffusion Transformer, to achieve Frame In and Frame Out in the cinematic domain. Model Zoo 🤗 | Model | Description | Huggingface | |--------------------------------------------------------------- | -------------------------------| ------------------------------------------------------------------------------------------------| | CogVideoX-I2V-5B V1.0 (Stage 1 - Motion Control) | Paper Weight v1.0 | Download | | CogVideoX-I2V-5B (Stage 2 - Motion + In-N-Out Control) | Paper Weight v1.0 | Download | | Wan2.2-TI2V-5B (Stage 1 - Motion Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.6 on Arbitrary Resolution | Download |

license:gpl-3.0
36
3

FrameINO_CogVideoX_Stage1_Motion_v1.0

Frame In-N-Out: Unbounded Controllable Image-to-Video Generation Frame In-N-Out is a controllable Image-to-Video generation Diffusion Transformer model where objects can enter or exit the scene along user-specified motion trajectories and ID reference. Our method introduces a new dataset curation pattern recognition, evaluation protocol, and a motion-controllable , identity-preserving , unbounded canvas Video Diffusion Transformer, to achieve Frame In and Frame Out in the cinematic domain. Model Zoo 🤗 | Model | Description | Huggingface | |--------------------------------------------------------------- | -------------------------------| ------------------------------------------------------------------------------------------------| | CogVideoX-I2V-5B V1.0 (Stage 1 - Motion Control) | Paper Weight v1.0 | Download | | CogVideoX-I2V-5B (Stage 2 - Motion + In-N-Out Control) | Paper Weight v1.0 | Download | | Wan2.2-TI2V-5B (Stage 1 - Motion Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.5 on 704P | Download | | Wan2.2-TI2V-5B (Stage 2 - Motion + In-N-Out Control) | New Weight v1.6 on Arbitrary Resolution | Download |

license:gpl-3.0
28
3

FrameINO_Wan2.2_5B_Stage2_MotionINO_v1.6

NaNK
license:gpl-3.0
9
1