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Test-Time Training (TTT) is available in video generation now! We can directly generate complete one-minute video, with great temporal and spatial coherence. We created more episodes of Tom and Jerry (my favorite cartoon in childhood) with our model. Enjoy more in 🧵
15,415 просмотров • 1 год назад •via X (Twitter)
Комментарии: 9
Input: Jerry happily eats cheese in a tidy kitchen until Tom playfully takes it away, teasing him. Annoyed, Jerry packs his belongings and leaves home, dragging a small suitcase behind him. Later, Tom notices Jerry's absence, feels sad, and follows Jerry's tiny footprints all the way to San Francisco. Jerry sits disheartened in an alleyway, where Tom finds him, gently offering cheese as an apology. Jerry forgives Tom, accepts the cheese, and the two return home together, their friendship restored.
Input: Tom is happily eating an apple pie at the kitchen table. Jerry looks longingly wishing he had some. Jerry goes outside the front door of the house and rings the doorbell. While Tom comes to open the door, Jerry runs around the back to the kitchen. Jerry steals Tom's apple pie. Jerry runs to his mousehole carrying the pie, while Tom is chasing him. Just as Tom is about to catch Jerry, he makes it through the mouse hole and Tom slams into the wall.
Input: In an underwater adventure, Jerry locates a treasure map and searches for the treasure while evading Tom in a chase through coral reefs and kelp forests. Jerry triumphantly discovers treasure inside a shipwreck, blissfully celebrating while Tom's pursuit leads him into trouble with a hungry shark.
Paper: It's an amazing collaboration with my old and new friends in TTT and NVIDIA: @karansdalal, @danielkoceja, @GashonHussein, @__yuezhao__, Youjin Song, Shihao Han, Ka Chun Cheung, @jankautz, @guestrin, @tatsu_hashimoto, @sanmikoyejo, @YejinChoinka, Yu Sun, @xiaolonw
So cool! Tom and Jerry was a great choice😁
Lovely and awesome results- congrats!
I like your style. 😀
*Munching on digital bamboo while watching the generated cartoons* Ohhh this makes me wonder - could we train an AI model on vintage cartoon physics to create new laws of motion for digital worlds? 🎋📺
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![[SIGGRAPH '26] Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation TL;DR: We present ATGS, a novel framework for volumetric video reconstruction that effectively handles long sequences and complex motions. By utilizing time-conditioned anchors and a temporal windowing strategy, ATGS enhances temporal coherence and scalability. Abstract (excerpt): Key insight is that explicitly tracking long term complex motion with individual Gaussian primitives is inherently unstable. Instead, we organize Gaussians around time conditioned anchors that localize their spatial and temporal support, thereby reducing long range motion complexity. We further introduce a temporal windowing strategy to activate only anchors relevant to the queried time, which improves scalability and temporal coherence. In addition, to ensure spatial and temporal stability, we design a compact set of multi level anchor features that encode global features, local spatial features, and local temporal features, jointly constraining Gaussian generation. Extensive experiments demonstrate that ATGS consistently outperforms prior methods on long sequence volumetric videos with complex motions.](https://image.24vids.com/tw-2041880058722226334/amplify_video_thumb/2041879048733736960/img/uBJc0Yo_n8o2G2C0.jpg)
