{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/liquid-warping-gan-a-unified-framework-for","title":"Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis","arxiv_id":"1909.12224","date":"2019-09-26","proceeding":"ICCV 2019 10","authors":["Wen Liu","Zhixin Piao","Jie Min","Wenhan Luo","Lin Ma","Shenghua Gao"],"abstract":"We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that propagates the source information in both image and feature spaces, and synthesizes an image with respect to the reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method is able to support a more flexible warping from multiple sources. In addition, we build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our method in several aspects, such as robustness in occlusion case and preserving face identity, shape consistency and clothes details. All codes and datasets are available on https://svip-lab.github.io/project/impersonator.html","url_abs":"https://arxiv.org/abs/1909.12224v3","url_pdf":"https://arxiv.org/pdf/1909.12224v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"liquid-warping-gan-a-unified-framework-for","repo_url":"https://github.com/CompVis/image2video-synthesis-using-cINNs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"liquid-warping-gan-a-unified-framework-for","repo_url":"https://github.com/svip-lab/impersonator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"appearance-transfer","task_name":"Appearance Transfer"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1909.12224","atlas_url":"https://app.syntology.ai/?focus=1909.12224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12224"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/CompVis/image2video-synthesis-using-cINNs","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/svip-lab/impersonator","reach":null}],"summary":{"ran_draft_wrong":3,"unverified":1},"by_repo_kind":{"listed":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"c99304fdadd5d523","entry":"parse_view_params","repo":"svip-lab/impersonator","repo_kind":"listed","path":"demo_view.py","file_url":"https://github.com/svip-lab/impersonator/blob/HEAD/demo_view.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"c99304fdadd5d523"}},{"code_sha256_prefix":"36eadc3d237b7174","entry":"tensor2cv2","repo":"svip-lab/impersonator","repo_kind":"listed","path":"demo_swap.py","file_url":"https://github.com/svip-lab/impersonator/blob/HEAD/demo_swap.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"36eadc3d237b7174"}},{"code_sha256_prefix":"323d2a36cfa44412","entry":"tensor2cv2","repo":"svip-lab/impersonator","repo_kind":"listed","path":"demo_view.py","file_url":"https://github.com/svip-lab/impersonator/blob/HEAD/demo_view.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"323d2a36cfa44412"}},{"code_sha256_prefix":"5336503e4f6bebfd","entry":"load_mixamo_smpl","repo":"svip-lab/impersonator","repo_kind":"listed","path":"demo_imitator.py","file_url":"https://github.com/svip-lab/impersonator/blob/HEAD/demo_imitator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5336503e4f6bebfd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}