Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: 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.






review_title: Official Blind Review #4

final_score: rating: 6: Weak Accept
review_content: This submission proposes an application of meta-learning to video frame generation modeling conditioned on human pose information, in order to allow the model to adapt to the context of each video. This context is provided in the form of a support set of K pairs of pose/frame images for the video. Reptile is used as the meta-learning method, and applied to two recently proposed video-frame generative networks (Pix2PixHD and Posewarp). In both cases, results show that Reptile is able to produce better adaptive models, i.e. models that when fine-tuned on the support set produce better image frames.

Though the originality of the work is somewhat weak (it's a relatively straightforward application of Reptile to Pix2PixHD and Posewarp), the problem setting is novel and I find the demonstration that Reptile works well in this setting interesting and valuable. The paper is also clearly written and easy to follow. For these reasons, I'm personally leaning towards recommending to accept this submission.
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: In this paper, authors propose to address few shot video retargeting, where one should adapt a generic generative model of human actions to a specific person given a few samples of their appearance. 

Overall, the paper is written with a good structure. I do like the problem setting and motivations in this paper. However, the solution is not quite novel for me. Both base model (Pix2PixHD) and few-shot adaptation (Reptile) come from the previous works. Their combination is somewhat incremental.  
review_title: Official Blind Review #3

Meta-review: Three reviewers have assessed this paper and they have scored it 6/6/6 after rebuttal. Nonetheless, the reviewers have raised a number of criticisms and the authors are encouraged to resolve them for the camera-ready submission.
Decision: Accept (Poster)