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Context: The large-scale multimodal dataset [35] with billions of text-image pairs crawled from Internet has enabled breakthrough in open-domain Text-to-Image (T2I) generation [25, 29, 4, 6, 34]. To replicate this success in Text-to-Video (T2V) generation, recent works [36, 14, 17, 48] extend the spatial-only T2I generation models to the spatio-temporal domain.These models generally adopt the standard paradigm to finetune T2I models on large-scale text-video datasets (e.g., WebVid-10M [2]).Although this paradigm produces decent results for T2V generation, it requires extensive training, which is expensive and not affordable to everyone. Human is capable of one-shot learning [22, 23, 42]. For instance, given a video paired with a textual description of “a man is skiing on snow” as a hint, we might hallucinate how a panda skis on snow if we could picture what a panda looks like.Since T2I models pre-trained with large-scale image-text data already capture the knowledge of open-domain concepts,an intuitive question arises:can it infer other novel videos from a single video example like human beings?A new T2V generation problem is therefore introduced, namely, One-Shot Video Generation, where only a single text-video pair is presented for training an open-domain T2V generator.The generator is expected to capture necessary motion knowledge from the input video and synthesize novel videos guided by edited prompts. One of the applications of our Tune-A-Video is to replace the subject in the training video. As shown in Fig. 7, Tune-A-Video is able to generate videos with customized subjects via changing the corresponding terms in text prompt, for example, replacing the polar bear with mammoth (the 2nd row of Fig. 7) in the “walking bear” example; replacing the man with King Kong or astronaut (the 7th and 8th row of Fig. 7) in the “running man” example.The generated videos are consistent in time and well-aligned with the modified text prompts. As shown in Fig. 9, the resultant video of “a woman is running on the lawn” generated by our Tune-A-Video trained on one text-video pair of “a man running on the beach”, is comparable to the one generated by CogVideo [19] trained on large-scale datasets. When we challenge the models using a more complex prompt, e.g., “a cat is running on the single-plank bridge, comic style”, our Tune-A-Video generates continuous video frames that are better aligned with the target prompt. This demonstrates that our model integrates the motion guidance from training video with the rich semantics of pre-trained T2I models for high temporal consistency and deep language understanding. See Fig. 15 for more qualitative comparisons with CogVideo. The models without One-Shot Tuning uses the weight of pre-trained T2I for inference. The tuning itself (the 2nd row of Fig. 10) captures spatial information and yields similar semantics as the training video (e.g., the panda skis like the man in training video). This implies that our one-shot tuning strategy is capable of performing semantic mixing as [24, 21], but with more flexibility that the object categories can be replaced. However, the temporal consistency cannot be maintained through individual frame attention (e.g., the pandas in the 2nd row of Fig. 10 are inconsistent). Although our Tune-A-Video w/o tuning (the 3nd row of Fig. 10) output consistent content across frames, it does not have a notion of motion since it is only trained on static images. With tuning, our full model (the 4nd row of Fig. 10) is able to generate the temporally-coherent video that incorporates the motion information (i.e., skiing) in the training video, demonstrating the effectiveness of our One-Shot Tuning. To imitate how humans adapt new visual concepts from a single exemplar, we introduce a new T2V generation task called One-Shot Video Generation, where only a single text-video pair is provided for training an open-domain T2V generator.To this end, we propose Tune-A-Video, a simple yet effective solution based on pretrained T2I diffusion models.Our method exploits the properties of pretrained T2I models by extending spatial self-attention to spatio-temporal domain with a tailored Sparse-Causal Attention.To capture necessary motion knowledge, we update the projection matrices in attention block on one training sample. Given this, our Tune-A-Video supports several T2V applications, including subject replacement, background change, attribute modification, style transfer, and etc.

Question: Just out of curiosity, how humans are so well at this? Cab human's technique be used for the machines as well? or do we need a totally different approach?
One-Shot Tuning acquires temporal knowledge from one training video, which is enabled by Sparse-Causal Attention (SCAttn) and temporal self-attention (Temp-Attn). It aptures spatial information and yields similar semantics as the training video to perform semantic mixing.