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Image Animation with Keypoint Mask

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Or Toledano, Yanir Marmor, Dov Gertz

Computer Vision and Pattern Recognition · Submitted 20 Dec 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We suggest two variants of the structure from different steps in the keypoint module, and show superior qualitative pose and quantitative scores.

how it is stated · asserted 0.98 read the record →

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arXiv ID2112.10457 (version 2)
Pith IDNP2GVZWC, Pith's permanent record of this paper and its review
CitationDov Gertz, Or Toledano, Yanir Marmor. "Image Animation with Keypoint Mask." arXiv:2112.10457 (2021). https://pith.science/paper/NP2GVZWC
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,169 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.18 · human participants 0.03 · controlled comparison 0.15 · randomized assignment 0.01 · states quantitative result 0.09 · compares with prior results 0.43 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. In order to solve it, we face the challenging complexity of motion representation and the unknown relations between the driving video and the source image. Despite its difficulty, this problem attracted great interests from researches at the recent years, with gradual improvements. The goal is often thought as the decoupling of motion and appearance, which is may be solved by extracting the motion from keypoint movement. We chose to tackle the generic, unsupervised setting, where we need to apply animation to any arbitrary object, without any domain specific model for the structure of the input. In this work, we extract the structure from a keypoint heatmap, without an explicit motion representation. Then, the structures from the image and the video are extracted to warp the image according to the video, by a deep generator. We suggest two variants of the structure from different steps in the keypoint module, and show superior qualitative pose and quantitative scores.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. background 1.00 0.05 0.03 asserted 1.00
s1 In order to solve it, we face the challenging complexity of motion representation and the unknown relations between the driving video and the source image. background 0.76 0.13 0.02 asserted 1.00
s2 Despite its difficulty, this problem attracted great interests from researches at the recent years, with gradual improvements. background 1.00 0.04 0.02 asserted 1.00
s3 The goal is often thought as the decoupling of motion and appearance, which is may be solved by extracting the motion from keypoint movement. background 0.95 0.07 0.06 hedged possibility 0.99
s4 We chose to tackle the generic, unsupervised setting, where we need to apply animation to any arbitrary object, without any domain specific model for the structure of the input. contribution 0.50 0.74 0.04 asserted 0.98
s5 In this work, we extract the structure from a keypoint heatmap, without an explicit motion representation. method 0.94 0.94 0.11 asserted 1.00
s6 Then, the structures from the image and the video are extracted to warp the image according to the video, by a deep generator. method 1.00 0.87 0.08 asserted 0.99
s7 We suggest two variants of the structure from different steps in the keypoint module, and show superior qualitative pose and quantitative scores. result 0.85 0.96 0.69 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.90.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. In order to solve it, we face the challenging complexity of motion representation and the unknown relations between the driving video and the source image. Despite its difficulty, this problem attracted great interests from researches at the recent years, with gradual improvements. The goal is often thought as the decoupling of motion and appearance, which is may be solved by extracting the motion from keypoint movement. We chose to tackle the generic, unsupervised setting, where we need to apply animation to any arbitrary object, without any domain specific model for the structure of the input. In this work, we extract the structure from a keypoint heatmap, without an explicit motion representation. Then, the structures from the image and the video are extracted to warp the image according to the video, by a deep generator. We suggest two variants of the structure from different steps in the keypoint module, and show superior qualitative pose and quantitative scores.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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