Target-Aware Video Diffusion Models
We present a target-aware video diffusion model that generates videos from an
input image in which an actor interacts with a specified target while
performing a desired action. The target is defined by a segmentation mask and
the desired action is described via a text prompt. Unlike existing controllable
image-to-video diffusion models that often rely on dense structural or motion
cues to guide the actor's movements toward the target, our target-aware model
requires only a simple mask to indicate the target, leveraging the
generalization capabilities of pretrained models to produce plausible actions.
This makes our method particularly effective for human-object interaction (HOI)
scenarios, where providing precise action guidance is challenging, and further
enables the use of video diffusion models for high-level action planning in
applications such as robotics. We build our target-aware model by extending a
baseline model to incorporate the target mask as an additional input. To
enforce target awareness, we introduce a special token that encodes the
target's spatial information within the text prompt. We then fine-tune the
model with our curated dataset using a novel cross-attention loss that aligns
the cross-attention maps associated with this token with the input target mask.
To further improve performance, we selectively apply this loss to the most
semantically relevant transformer blocks and attention regions. Experimental
results show that our target-aware model outperforms existing solutions in
generating videos where actors interact accurately with the specified targets.
We further demonstrate its efficacy in two downstream applications: video
content creation and zero-shot 3D HOI motion synthesis.
67ee9fb0ffcd7f6fd1bfffbc
Taeksoo Kim
67ee9fb0ffcd7f6fd1bfffbd
Hanbyul Joo
Target-Aware Video Diffusion Models
We present a target-aware video diffusion model that generates videos from an
input image in which an actor interacts with a specified target while
performing a desired action. The target is defined by a segmentation mask and
the desired action is described via a text prompt. Unlike existing controllable
image-to-video diffusion models that often rely on dense structural or motion
cues to guide the actor's movements toward the target, our target-aware model
requires only a simple mask to indicate the target, leveraging the
generalization capabilities of pretrained models to produce plausible actions.
This makes our method particularly effective for human-object interaction (HOI)
scenarios, where providing precise action guidance is challenging, and further
enables the use of video diffusion models for high-level action planning in
applications such as robotics. We build our target-aware model by extending a
baseline model to incorporate the target mask as an additional input. To
enforce target awareness, we introduce a special token that encodes the
target's spatial information within the text prompt. We then fine-tune the
model with our curated dataset using a novel cross-attention loss that aligns
the cross-attention maps associated with this token with the input target mask.
To further improve performance, we selectively apply this loss to the most
semantically relevant transformer blocks and attention regions. Experimental
results show that our target-aware model outperforms existing solutions in
generating videos where actors interact accurately with the specified targets.
We further demonstrate its efficacy in two downstream applications: video
content creation and zero-shot 3D HOI motion synthesis.
A target-aware video diffusion model generates accurate actor-target interactions from images using segmentation masks and text prompts, outperforming existing methods in HOI scenarios and video applications.
video diffusion model segmentation mask text prompt controllable image-to-video diffusion models human-object interaction HOI scenarios robotics cross-attention loss transformer blocks attention regions zero-shot 3D HOI motion synthesis