MetaPix: Few-Shot Video Retargeting
We address the task of unsupervised retargeting of human actions from one video to another. We consider the challenging setting where only a few frames of the target is available. The core of our approach is a conditional generative model that can transcode input skeletal poses (automatically extracted with an off-the-shelf pose estimator) to output target frames. However, it is challenging to build a universal transcoder because humans can appear wildly different due to clothing and background scene geometry. Instead, we learn to adapt – or personalize – a universal generator to the particular human and background in the target. To do so, we make use of meta-learning to discover effective strategies for on-the-fly personalization. One significant benefit of meta-learning is that the personalized transcoder naturally enforces temporal coherence across its generated frames; all frames contain consistent clothing and background geometry of the target. We experiment on in-the-wild internet videos and images and show our approach improves over widely-used baselines for the task.
This paper proposes a novel and interesting task that learn to retarget human actions with few-shot samples. The overall pipeline is built by applying meta-learning strategy on pre-trained retargeting module. It follows a conditional generator and discriminator structure that leverages few-shot frames to retarget the action of source video. The approach is technically sound. The evaluations are compared with two baseline methods, Pix2PixHD and Posewarp. Their evaluations are satisfactory and convincing, the results demonstrate some improvements over baseline models regarding both the selected metrics and the visualizing performance. Though the proposed problem is novel and somewhat interesting, there are also several weaknesses of this work: - The novelty of methodology is somewhat limited. It is more about merging several state-of-the-art modules in different tasks to tackle the few-shot retargeting problem. Though efforts may be needed to make the pipeline work, the overall contribution is not significant. - The improvement obtained with proposed MetaPix module is not significant in few-shot setting according to Table 1. Additionally, could the authors provide some visualizing results for different K number, which would be interesting for analysis.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
MetaPix: Few-Shot Video Retargeting
We address the task of unsupervised retargeting of human actions from one video to another. We consider the challenging setting where only a few frames of the target is available. The core of our approach is a conditional generative model that can transcode input skeletal poses (automatically extracted with an off-the-shelf pose estimator) to output target frames. However, it is challenging to build a universal transcoder because humans can appear wildly different due to clothing and background scene geometry. Instead, we learn to adapt – or personalize – a universal generator to the particular human and background in the target. To do so, we make use of meta-learning to discover effective strategies for on-the-fly personalization. One significant benefit of meta-learning is that the personalized transcoder naturally enforces temporal coherence across its generated frames; all frames contain consistent clothing and background geometry of the target. We experiment on in-the-wild internet videos and images and show our approach improves over widely-used baselines for the task.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes a novel and interesting task that learn to retarget human actions with few-shot samples. The overall pipeline is built by applying meta-learning strategy on pre-trained retargeting module. It follows a conditional generator and discriminator structure that leverages few-shot frames to retarget the action of source video. The approach is technically sound. The evaluations are compared with two baseline methods, Pix2PixHD and Posewarp. Their evaluations are satisfactory and convincing, the results demonstrate some improvements over baseline models regarding both the selected metrics and the visualizing performance. Though the proposed problem is novel and somewhat interesting, there are also several weaknesses of this work: - The novelty of methodology is somewhat limited. It is more about merging several state-of-the-art modules in different tasks to tackle the few-shot retargeting problem. Though efforts may be needed to make the pipeline work, the overall contribution is not significant. - The improvement obtained with proposed MetaPix module is not significant in few-shot setting according to Table 1. Additionally, could the authors provide some visualizing results for different K number, which would be interesting for analysis.
assistant
This paper proposes a novel and interesting task that learn to retarget human actions with few-shot samples. The overall pipeline is built by applying meta-learning strategy on pre-trained retargeting module. It follows a conditional generator and discriminator structure that leverages few-shot frames to retarget the action of source video. The approach is technically sound. The evaluations are compared with two baseline methods, Pix2PixHD and Posewarp. Their evaluations are satisfactory and convincing, the results demonstrate some improvements over baseline models regarding both the selected metrics and the visualizing performance. Though the proposed problem is novel and somewhat interesting, there are also several weaknesses of this work: - The novelty of methodology is somewhat limited. It is more about merging several state-of-the-art modules in different tasks to tackle the few-shot retargeting problem. Though efforts may be needed to make the pipeline work, the overall contribution is not significant. - The improvement obtained with proposed MetaPix module is not significant in few-shot setting according to Table 1. Additionally, could the authors provide some visualizing results for different K number, which would be interesting for analysis.