You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: 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. Formally, given a video \mathcal{V}=\left\{v_{i}|i\in[1,m]\right\} with m frames, accompanied with a corresponding textual description \mathcal{T}, our objective is to generate novel videos \hat{\mathcal{V}} driven by text prompts \hat{\mathcal{T}} using a pre-trained T2I model M, i.e., M(\hat{\mathcal{V}}|\mathcal{V},\mathcal{T},\hat{\mathcal{T}}), where \mathcal{T} and \hat{\mathcal{T}} share the same verbs.Examples of output variations driven by \hat{\mathcal{T}} include: changes of subject, background (e.g., the place where the subject is), attribute (e.g., color, age, etc.), and other semantic modifications (see Fig. 1 and Fig. 7). We provide several applications of our Tune-A-Video in generating videos with novel visual concepts (e.g., subjects, backgrounds, attributes, styles, etc.) guided by the text prompt. Here, we showcase two training examples: “a polar bear is walking on ice” (“walking bear”, the 1st row of Fig. 7), “a young man is running on the beach” (“running man”, the 5th row of Fig. 7), and their variations with edited prompts. More examples can be found in Fig. 12-14. We examine the ability of our Tune-A-Video in modifying the attributes such as color (e.g., a man wearing yellow to be in red, the 2nd row of Fig. 1) and age (e.g., a young man to become old, the 6th row of Fig. 7). We found that our Tune-A-Video even enables modifying the attribute that is not specified in the training prompt (e.g., we do not tell the model the skiing man in the training video wears yellow clothes). Notably, other properties in training video could be well preserved (e.g., the black backpack wore by the skiing man, and the blue shirts and black shorts wore by the running man). Thanks to the broad knowledge of pertrained T2I models, Tune-A-Video is able to transfer videos into different styles, which cannot be easily learned from video data [36]. By appending the style description (e.g., comic style) to the sentence, our Tune-A-Video yields outputs in that style with consistent motions and semantics (the 4th and 8th row of Fig .7). 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: How about other terms like adjective?
The authors examine the ability of attribute modification with the adjectives like color annd chage. Also, style transfer demonstrates the examples.