6 DAILY CVPR Sunday Even with an innovative new model in hand and a Best Paper nomination on the table, Yiqing remains grounded. “All of us are very honored to be an award candidate, but I think it’s not only because of the quality of the work, but it’s also because of luck,” she says modestly. “There are many, many works that are very, very high quality.” What made this one stand out, she suggests, is its perspective: “We’re looking at a classic problem through a modern lens. I’ve worked on popular methods like NeRF and Gaussian splatting before, and the main bottleneck is the inference time learning - you wait minutes, hours, even days. Classical methods don’t have that problem. Now, classical methods are generalizable, so we try to marry the two trends together to create a new possibility.” Looking ahead, Yiqing sees several promising directions for future work, which she hopes the community will take forward. First, there is the potential to scale the dataset even further, not just in terms of size, but also in terms of diversity. Incorporating noisy real-world data would be particularly valuable. “One million sounds big,” she remarks, “but it’s still small compared to what's used for diffusion models.” Next is extending the model’s capabilities beyond geometry and scene flow. “We’re interested in predicting other modalities, like camera motion, to decompose scene motion into different fractions for more applications,” she tells us. The method could also be extended to long-term tracking. “Right now, we work with image pairs, but what if we had more pairs? What if we had a longer time horizon between the pairs?” She is also excited about potential applications in robotics: “People have been trying to use particle systems in robotics because they found Oral & Award Candidate
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