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Accurately and efficiently modeling dynamic scenes and motions is consideredso challenging a task due to temporal dynamics and motion complexity. Toaddress these challenges, we propose DynMF, a compact and efficientrepresentation that decomposes a dynamic scene into a few neural trajectories.We argue that the per-point motions of a dynamic scene can be decomposed into asmall set of explicit or learned trajectories. Our carefully designed neuralframework consisting of a tiny set of learned basis queried only in time allowsfor rendering speed similar to 3D Gaussian Splatting, surpassing 120 FPS, whileat the same time, requiring only double the storage compared to static scenes.Our neural representation adequately constrains the inherently underconstrainedmotion field of a dynamic scene leading to effective and fast optimization.This is done by biding each point to motion coefficients that enforce theper-point sharing of basis trajectories. By carefully applying a sparsity lossto the motion coefficients, we are able to disentangle the motions thatcomprise the scene, independently control them, and generate novel motioncombinations that have never been seen before. We can reach state-of-the-artrender quality within just 5 minutes of training and in less than half an hour,we can synthesize novel views of dynamic scenes with superior photorealisticquality. Our representation is interpretable, efficient, and expressive enoughto offer real-time view synthesis of complex dynamic scene motions, inmonocular and multi-view scenarios.

DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting