Tootfinder

All this brings me back to some text I was writing yesterday for my students, on which I’d appreciate any thoughtful feedback:
❝You can let the computer do the typing for you, but never let it do the thinking for you.
This is doubly true in the current era of AI hype. If the AI optimists are correct (the credible ones, anyway), software development will consist of humans critically evaluating, shaping, and correcting the output of LLMs. If the AI skeptics are correct, then the future will bring mountains of AI slop to decode, disentangle, fix, and/or rewrite. Either way, it is •understanding• and •critically evaluating• code — not merely •generating• it — that will be the truly essential ability. Always has been; will be even more so. •That• is what you are learning here.❞
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LIA-X: Interpretable Latent Portrait Animator
Yaohui Wang, Di Yang, Xinyuan Chen, Francois Bremond, Yu Qiao, Antitza Dantcheva
https://arxiv.org/abs/2508.09959 https://

LIA-X: Interpretable Latent Portrait Animator
We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an autoencoder that models motion transfer as a linear navigation of motion codes in latent space. Crucially, it incorporates a novel Sparse Motion Dictionary that enables the model to disentangle facial dynamics into interpretable factors. Deviating from previous 'warp-render' approaches, the interpretability of the Spars…

Hydrodynamics without Averaging -- a Hard Rods Study
Friedrich H\"ubner
https://arxiv.org/abs/2507.17827 https://arxiv.org/pdf/2507.17827

Hydrodynamics without Averaging -- a Hard Rods Study
On the example of the integrable hard rods model we study the quality of the (generalized) hydrodynamic approximation on a single coarse-grained sample. This is opposed to the traditional approach which averages over an appropriate local equilibrium state. While mathematically more ambiguous, a major advantage of the new approach is that it allows us to disentangle intrinsic diffusion from `diffusion from convection' effects. For the hard rods we find intrinsic diffusion is absent, which agrees…

HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
Byungjun Kim, Shunsuke Saito, Giljoo Nam, Tomas Simon, Jason Saragih, Hanbyul Joo, Junxuan Li
https://arxiv.org/abs/2507.19481

HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
We present a universal prior model for 3D head avatars with explicit hair compositionality. Existing approaches to build generalizable priors for 3D head avatars often adopt a holistic modeling approach, treating the face and hair as an inseparable entity. This overlooks the inherent compositionality of the human head, making it difficult for the model to naturally disentangle face and hair representations, especially when the dataset is limited. Furthermore, such holistic models struggle to su…