Browse State-of-the-Art › Facial expression generation
Facial expression generation
7 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
7 shown of 7 papers with code (22 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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22 Jan 2022 2 repositories listedFacial expressions are a form of non-verbal communication that humans perform seamlessly for meaningful transfer of information.
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21 Mar 2025 1 repository listedAutomatic robotic facial expression generation is crucial for human-robot interaction, as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors.
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27 Aug 2024 1 repository listedFacial expressions (non-manual), in particular, are responsible for encoding the grammar of the sentence to be spoken, applying punctuation, pronouns, or emphasizing signs.
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25 Jul 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedIn this work, we propose the use of AUs (action units) for facial expression control in face generation.
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29 Mar 2023 1 repository listedIn this paper, we introduce a generative framework for generating 3D facial expression sequences (i.
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17 Feb 2022 1 repository listedThe subtleness of human facial expressions and a large degree of variation in the level of intensity to which a human expresses them is what makes it challenging to robustly classify and generate images of facial…
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17 Nov 2015 1 repository listedContrary to a classical neural network, a belief network can predict more than the expected value of the output Y given the input X.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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