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Check out PhysHead: Simulation-Ready Gaussian Head Avatars (CVPR 2026)! We introduce a layered head & hair model using strands + Gaussian splats, enabling physics-based animation from multi-view video. PS: We didn’t dare compute our bald versions… maybe you’re braver 🙂

Gerard Pons-Moll

6,235 subscribers

10,232 Aufrufe • vor 5 Monaten •via X (Twitter)

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Gerard Pons-Mollvor 5 Monaten

Kudos to @bernakabadayi,@ness_pirs,@w_zielonka,@JustusThies !

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Matthias Niessner

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Davide Scaramuzza

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Curious Refuge

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Check out our #ECCV2026 paper "Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention", where we make linear attention sparse in space, recurrent in time, and parallel in training, enabling the first purely-linear-attention-based neural network for asynchronous object detection with #EventCameras, outperforming the previous best asynchronous method with 20x less computation with truly event-by-event inference on CPU! Code released! Paper: Code: Video: Event cameras promise extremely low-latency vision, but to fully exploit them, the neural network must be low-latency too. We introduce #SpatiallySparseLinearAttention (#SSLA) for asynchronous object detection directly from raw events. Linear attention is particularly appealing for event cameras: it can be trained efficiently in parallel on long event sequences, while at inference it operates recurrently, updating its prediction every time a new event arrives. The problem is that conventional linear attention updates its entire state for every event. For object detection, where fine spatial resolution matters, this quickly becomes expensive. Our key idea is simple: an event only carries information about a small spatial region, so why update the entire spatial state? SSLA updates only the relevant parts of the state, enabling fine-grained spatial representations while keeping per-event computation low. We achieve: - >20× lower per-event computation than the strongest prior asynchronous baseline - State-of-the-art accuracy among asynchronous object detection methods - Truly event-by-event inference on CPU, designed to preserve the latency advantage of event cameras Come to our poster on Friday September 11, 2026 from 4-6pm at ExHall #389 Reference: Haiqing Hao, Zhipeng Sui, Rong Zou, Zijia Dai, Nikola Zubić, Davide Scaramuzza, Wenhui Wang Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention ECCV, 2026 Prophesee SynSense University of Zurich UZH Science European Research Council (ERC) UZHai UZH IfI Tesla BYD #EventCameras #ComputerVision #Robotics #DeepLearning #NeuromorphicVision #AI

Davide Scaramuzza

52,465 Aufrufe • vor 22 Tagen

$Super proud and excited to finally present LiteMol-1. This is our first foundation model from LiteFold, pre-trained from scratch. Today, LiteMol-1 can generate small molecules, peptides, cyclic peptides, depsipeptides, peptides with ncAAs, macrocycles, and PROTACs. Across our peptide and small-molecule evaluations, the model shows competitive results. In several settings, we are on-par with or better than frontier structure-based models, at a fraction of the generation cost. But the part I find most interesting is that this is a model for agents. We have seen ourselves how much compute, and how many tokens it can take to generate good binders using frontier structure-based models. Sometimes you need to generate tens of thousands of designs just to get a handful worth taking forward. Now put this inside an AutoResearch loop. The agent has to continuously parse structures, inspect PDB/CIF files, compare candidates, run evaluations, modify the design, and repeat the whole thing again. It becomes extremely expensive very quickly. Sequence space gives us a very different interface. LLMs are much more efficient at inspecting and manipulating compact molecular representations like SMILES than repeatedly operating over full structural files. So LiteMol-1 becomes something like an infinite molecular canvas for the agent. For a given target and objective, the model can continuously propose what a biomolecule could look like. The agent can inspect those generations, take inspiration from them, preserve certain regions, edit others, optimize them, score them, and generate again. Generate → inspect → evaluate → edit → generate again. There is another problem I care a lot about. Most molecule design models today are heavily optimized around binding. But binding is only one part of whether something eventually becomes a therapeutic. What about ADME? Toxicity? Selectivity? Solubility? Membrane permeability? Synthesizability? For this, we also built a Monte Carlo Tree Search-based multi-objective generation framework around LiteMol-1. Instead of combining everything into one score, the search keeps multiple strong candidates, each balancing the desired properties in a different way. As our scoring functions and verifiers get better, the generation system gets better too. We can start steering molecules not just toward “binds well”, but toward a broader therapeutic design specification. The bottleneck slowly moves from simply generating molecules to having sufficiently good verifiers and scoring functions to tell us what is actually worth generating. Check out our technical research blog post for all the details. At LiteFold, our research is focused on engineering biomolecules and building systems that can carefully forecast their pre-clinical success. To stay updated on our research, follow LiteFold. Cheers!$

Super proud and excited to finally present LiteMol-1. This is our first foundation model from LiteFold, pre-trained from scratch. Today, LiteMol-1 can generate small molecules, peptides, cyclic peptides, depsipeptides, peptides with ncAAs, macrocycles, and PROTACs. Across our peptide and small-molecule evaluations, the model shows competitive results. In several settings, we are on-par with or better than frontier structure-based models, at a fraction of the generation cost. But the part I find most interesting is that this is a model for agents. We have seen ourselves how much compute, and how many tokens it can take to generate good binders using frontier structure-based models. Sometimes you need to generate tens of thousands of designs just to get a handful worth taking forward. Now put this inside an AutoResearch loop. The agent has to continuously parse structures, inspect PDB/CIF files, compare candidates, run evaluations, modify the design, and repeat the whole thing again. It becomes extremely expensive very quickly. Sequence space gives us a very different interface. LLMs are much more efficient at inspecting and manipulating compact molecular representations like SMILES than repeatedly operating over full structural files. So LiteMol-1 becomes something like an infinite molecular canvas for the agent. For a given target and objective, the model can continuously propose what a biomolecule could look like. The agent can inspect those generations, take inspiration from them, preserve certain regions, edit others, optimize them, score them, and generate again. Generate → inspect → evaluate → edit → generate again. There is another problem I care a lot about. Most molecule design models today are heavily optimized around binding. But binding is only one part of whether something eventually becomes a therapeutic. What about ADME? Toxicity? Selectivity? Solubility? Membrane permeability? Synthesizability? For this, we also built a Monte Carlo Tree Search-based multi-objective generation framework around LiteMol-1. Instead of combining everything into one score, the search keeps multiple strong candidates, each balancing the desired properties in a different way. As our scoring functions and verifiers get better, the generation system gets better too. We can start steering molecules not just toward “binds well”, but toward a broader therapeutic design specification. The bottleneck slowly moves from simply generating molecules to having sufficiently good verifiers and scoring functions to tell us what is actually worth generating. Check out our technical research blog post for all the details. At LiteFold, our research is focused on engineering biomolecules and building systems that can carefully forecast their pre-clinical success. To stay updated on our research, follow LiteFold. Cheers!

Anindyadeep

53,916 Aufrufe • vor 27 Tagen

#WATCH | Mumbai, Maharashtra: When asked about how 3:15 PM cutoffs and closing auction price spreads are disrupting his algorithmic hedging and intraday risk management, with Securities and Exchange Board of India (SEBI) confirming Closing Auction Session (CAS) is permanent, Shrikant Chouhan, Head of Equity Research, Kotak Securities, says, "...If we go through with the CAS-related issues which are giving some kind of uneasiness for option traders... this time option traders, they are mainly affected. The reason is that between this time gap, the prices change a lot, and because of that, most of the time on the day of expiry, especially on the day of expiry, it actually impacts option traders, those who are like writers. Broadly, we are of the view that at present SEBI is holding a lot of meetings with brokers, traders, algo traders, and they are designing their framework... they are ready to make changes as per the difficulties which traders are facing... broadly, I am of the view that it may take some time, but in the next maybe 1-2 months, definitely something will come out concrete, and based on that, again, we can see normalisation in the market... till then, option traders, especially writers or those who are using algorithms, they are trying to stay away from the market because of which volatility has also come down sharply in the market. You can see the market is falling gradually, but at the same time, intraday volatility is very low. Even if we consider Volatility Index (VIX), India's VIX index, that is also close to 11 or maybe 11.5 sort of levels, which is the lowest... it means that option traders are not active in this market. That is why I am of the view that it will take some time to come to normalcy. But eventually, definitely, we will come out with something that will help traders, option traders, I mean to say, in terms of managing their risk. It's not that they can increase their volume and all because our government is primarily focusing on controlling this particular speculative activity... they are trying to control that, and maybe because of that, I think they will come out with something which is very balanced."

#WATCH | Mumbai, Maharashtra: When asked about how 3:15 PM cutoffs and closing auction price spreads are disrupting his algorithmic hedging and intraday risk management, with Securities and Exchange Board of India (SEBI) confirming Closing Auction Session (CAS) is permanent, Shrikant Chouhan, Head of Equity Research, Kotak Securities, says, "...If we go through with the CAS-related issues which are giving some kind of uneasiness for option traders... this time option traders, they are mainly affected. The reason is that between this time gap, the prices change a lot, and because of that, most of the time on the day of expiry, especially on the day of expiry, it actually impacts option traders, those who are like writers. Broadly, we are of the view that at present SEBI is holding a lot of meetings with brokers, traders, algo traders, and they are designing their framework... they are ready to make changes as per the difficulties which traders are facing... broadly, I am of the view that it may take some time, but in the next maybe 1-2 months, definitely something will come out concrete, and based on that, again, we can see normalisation in the market... till then, option traders, especially writers or those who are using algorithms, they are trying to stay away from the market because of which volatility has also come down sharply in the market. You can see the market is falling gradually, but at the same time, intraday volatility is very low. Even if we consider Volatility Index (VIX), India's VIX index, that is also close to 11 or maybe 11.5 sort of levels, which is the lowest... it means that option traders are not active in this market. That is why I am of the view that it will take some time to come to normalcy. But eventually, definitely, we will come out with something that will help traders, option traders, I mean to say, in terms of managing their risk. It's not that they can increase their volume and all because our government is primarily focusing on controlling this particular speculative activity... they are trying to control that, and maybe because of that, I think they will come out with something which is very balanced."

ANI

37,157 Aufrufe • vor 1 Monat