We introduce bounded generation as a generalized task to control video
generation to synthesize arbitrary camera and subject motion based only on a
given start and end frame. Our objective is to fully leverage the inherent
generalization capability of an image-to-video model without additional
training or fine-tuning of the original model. This is achieved through the
proposed new sampling strategy, which we call Time Reversal Fusion, that fuses
the temporally forward and backward denoising paths conditioned on the start
and end frame, respectively. The fused path results in a video that smoothly
connects the two frames, generating inbetweening of faithful subject motion,
novel views of static scenes, and seamless video looping when the two bounding
frames are identical. We curate a diverse evaluation dataset of image pairs and
compare against the closest existing methods. We find that Time Reversal Fusion
outperforms related work on all subtasks, exhibiting the ability to generate
complex motions and 3D-consistent views guided by bounded frames. See project
page at this https URL

我们引入有限生成作为一种广义控制视频生成的任务，仅基于给定的起始和结束帧来合成任意的摄像机和主题运动，通过新提出的 Time Reversal Fusion 采样策略完全充分利用图像到视频模型的内在泛化能力，无需额外的训练或原始模型的微调，生成平滑连接两个帧的视频，生成忠实的主题运动中间形态，静态场景的新视角，以及在两个边界帧相同时无缝视频循环。我们构建了一个多样化的评估数据集，并与最接近的现有方法进行比较，发现 Time Reversal Fusion 在所有子任务上表现优于相关工作，具有生成由有限帧引导的复杂运动和三维一致视图的能力。详见此 https URL 的项目页面。