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Andrea Tagliasacchi

@taiyasaki • 17,346 subscribers

Principal Scientist @ Wayve, Associate Professor @ SFU (research chair), Associate Professor @ UofT (status only)

Shorts

📢📢📢introducing 𝐏𝐨𝐰𝐞𝐫 𝐅𝐨𝐚𝐦 A 3D representation that can be ray traced or rasterized in real time, with NO COMPROMISE in quality. - Project: - arXiv: Rasterized at 3DGS-class FPS Ray traced at Radiant Foam speeds

📢📢📢introducing 𝐏𝐨𝐰𝐞𝐫 𝐅𝐨𝐚𝐦 A 3D representation that can be ray traced or rasterized in real time, with NO COMPROMISE in quality. - Project: - arXiv: Rasterized at 3DGS-class FPS Ray traced at Radiant Foam speeds

98,503 views

📢📢📢 𝐒𝐩𝐡𝐞𝐫𝐢𝐜𝐚𝐥 𝐕𝐨𝐫𝐨𝐧𝐨𝐢: directional appearance as a differentiable partition of the sphere – Project: – Paper: – Colab: Project led by 𝐅𝐫𝐚𝐧𝐜𝐞𝐬𝐜𝐨 𝐝𝐢 𝐒𝐚𝐫𝐢𝐨 (graduating!)

📢📢📢 𝐒𝐩𝐡𝐞𝐫𝐢𝐜𝐚𝐥 𝐕𝐨𝐫𝐨𝐧𝐨𝐢: directional appearance as a differentiable partition of the sphere – Project: – Paper: – Colab: Project led by 𝐅𝐫𝐚𝐧𝐜𝐞𝐬𝐜𝐨 𝐝𝐢 𝐒𝐚𝐫𝐢𝐨 (graduating!)

136,305 views

📢📢📢 𝐋𝐚𝐠𝐫𝐚𝐧𝐠𝐢𝐚𝐧 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐞𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 at #ECCV2024 Project led by: Shrisudhan Govindarajan (Shrisudhan Govindarajan) and Zeno Sambugaro (Zeno Sambugaro)

📢📢📢 𝐋𝐚𝐠𝐫𝐚𝐧𝐠𝐢𝐚𝐧 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐞𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 at #ECCV2024 Project led by: Shrisudhan Govindarajan (Shrisudhan Govindarajan) and Zeno Sambugaro (Zeno Sambugaro)

48,546 views

📢📢📢 Today I'll be giving a talk at the #CVPR2024 workshop on "Learning 3D with Multi-View Supervision" See you at 16:20 in Summit 331 Get a preview of the upcoming "3D Gaussian Splatting as Markov Chain Monte Carlo"

📢📢📢 Today I'll be giving a talk at the #CVPR2024 workshop on "Learning 3D with Multi-View Supervision" See you at 16:20 in Summit 331 Get a preview of the upcoming "3D Gaussian Splatting as Markov Chain Monte Carlo"

47,178 views

📢📢📢 NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization #3DV2025 TL;DR: neural reconstruction with a simpler architecture (no linear systems to solve), and up to 3x speedup vs. voxel-based methods!

📢📢📢 NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization #3DV2025 TL;DR: neural reconstruction with a simpler architecture (no linear systems to solve), and up to 3x speedup vs. voxel-based methods!

16,962 views

Videos

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📢📢📢 "𝐑𝐚𝐝𝐢𝐚𝐧𝐭 𝐅𝐨𝐚𝐦: Real-Time Differentiable Ray Tracing", a mesh-based 3D represention. Co-lead by my PhD students Shrisudhan Govindarajan and Daniel Rebain, and w/ Kwang Moo Yi

📢📢📢 "𝐑𝐚𝐝𝐢𝐚𝐧𝐭 𝐅𝐨𝐚𝐦: Real-Time Differentiable Ray Tracing", a mesh-based 3D represention. Co-lead by my PhD students Shrisudhan Govindarajan and Daniel Rebain, and w/ Kwang Moo Yi

Andrea Tagliasacchi

115,169 views • 1 year ago

📢📢📢 𝐓𝐫𝐢𝐚𝐧𝐠𝐥𝐞 𝐒𝐩𝐥𝐚𝐭𝐭𝐢𝐧𝐠+: 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐛𝐥𝐞 𝐑𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐎𝐩𝐚𝐪𝐮𝐞 𝐓𝐫𝐢𝐚𝐧𝐠𝐥𝐞𝐬. – Project: – ArXiv: – ⚠️Code released on October 8th

📢📢📢 𝐓𝐫𝐢𝐚𝐧𝐠𝐥𝐞 𝐒𝐩𝐥𝐚𝐭𝐭𝐢𝐧𝐠+: 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐛𝐥𝐞 𝐑𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐎𝐩𝐚𝐪𝐮𝐞 𝐓𝐫𝐢𝐚𝐧𝐠𝐥𝐞𝐬. – Project: – ArXiv: – ⚠️Code released on October 8th

Andrea Tagliasacchi

52,875 views • 1 year ago

📢📢📢 thrilled to announce "𝟒𝐃-𝐟𝐲: 𝐓𝐞𝐱𝐭-𝐭𝐨-𝟒𝐃 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐜𝐨𝐫𝐞 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠" Way to start Sherwin Bahmani 🎉 PhD co-advised with David Lindell at #UofT.

📢📢📢 thrilled to announce "𝟒𝐃-𝐟𝐲: 𝐓𝐞𝐱𝐭-𝐭𝐨-𝟒𝐃 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐜𝐨𝐫𝐞 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠" Way to start Sherwin Bahmani 🎉 PhD co-advised with David Lindell at #UofT.

Andrea Tagliasacchi

74,687 views • 2 years ago

📢📢📢 Introducing "𝐁𝐚𝐲𝐞𝐬' 𝐑𝐚𝐲𝐬: Uncertainty Quantification for Neural Radiance Fields" TL;DR: 𝐜𝐚𝐧 𝐈 "𝐭𝐫𝐮𝐬𝐭" 𝐦𝐲 𝐍𝐞𝐑𝐅 𝐚𝐭 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐱? with Lily Goli , @sellan_s, Cody Reading, Alec Jacobson.

📢📢📢 Introducing "𝐁𝐚𝐲𝐞𝐬' 𝐑𝐚𝐲𝐬: Uncertainty Quantification for Neural Radiance Fields" TL;DR: 𝐜𝐚𝐧 𝐈 "𝐭𝐫𝐮𝐬𝐭" 𝐦𝐲 𝐍𝐞𝐑𝐅 𝐚𝐭 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐱? with Lily Goli , @sellan_s, Cody Reading, Alec Jacobson.

Andrea Tagliasacchi

77,876 views • 3 years ago

📢📢📢 𝐀𝐂𝟑𝐃: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers TL;DR: for 3D camera control in generative video, it really helps knowing *which* part of your model you should mess with Internship by Sherwin Bahmani at Snap

📢📢📢 𝐀𝐂𝟑𝐃: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers TL;DR: for 3D camera control in generative video, it really helps knowing which part of your model you should mess with Internship by Sherwin Bahmani at Snap

Andrea Tagliasacchi

23,088 views • 1 year ago

📢📢📢 RoMo: Robust Motion Segmentation Improves Structure from Motion TL;DR: boost your SfM pipeline on dynamic scenes. We use epipolar cues + SAMv2 features to find robust masks for moving objects in a zero-shot manner. 🧵👇

📢📢📢 RoMo: Robust Motion Segmentation Improves Structure from Motion TL;DR: boost your SfM pipeline on dynamic scenes. We use epipolar cues + SAMv2 features to find robust masks for moving objects in a zero-shot manner. 🧵👇

Andrea Tagliasacchi

18,648 views • 1 year ago

📢📢📢 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐍𝐞𝐮𝐫𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐯𝐢𝐚 𝐒𝐨𝐟𝐭 𝐌𝐢𝐧𝐢𝐧𝐠 by Shakiba Kheradmand et al. TL;DR: importance sampling for accelerating your novel view synthesis workloads (...yes, it should also work for 3DGS)

📢📢📢 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐍𝐞𝐮𝐫𝐚𝐥 𝐅𝐢𝐞𝐥𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐯𝐢𝐚 𝐒𝐨𝐟𝐭 𝐌𝐢𝐧𝐢𝐧𝐠 by Shakiba Kheradmand et al. TL;DR: importance sampling for accelerating your novel view synthesis workloads (...yes, it should also work for 3DGS)

Andrea Tagliasacchi

21,828 views • 2 years ago

📢📢📢 TC4D: Trajectory-Conditioned Text-to-4D Generation Homepage: Lead by Sherwin Bahmani, with collaborators Xian Liu Yifan Wang Ivan Skorokhodov Victor Rong Ziwei Liu Xihui Liu Jeong Joon Park Sergey Tulyakov Gordon Wetzstein Andrea Tagliasacchi @CVPR David Lindell

📢📢📢 TC4D: Trajectory-Conditioned Text-to-4D Generation Homepage: Lead by Sherwin Bahmani, with collaborators Xian Liu Yifan Wang Ivan Skorokhodov Victor Rong Ziwei Liu Xihui Liu Jeong Joon Park Sergey Tulyakov Gordon Wetzstein Andrea Tagliasacchi @CVPR David Lindell

Andrea Tagliasacchi @CVPR

16,976 views • 2 years ago

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