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Dusting the Phantom: Gaussian Allocation for Fading Dynamic Content

Minhyeok Roh, Minseo Lee, Jun Young Jeong, Byeonghyeon Lee, Lucas Yunkyu Lee, Jaesik Park, Eunbyung Park

arXiv:2610.21302v1cs.CV

TL;DR

Challenging dynamic content can remain poorly reconstructed despite increased training or Gaussian capacity, and allocation strategies may miss both under-represented regions and useful temporal destinations. The paper adds Seed Injection and Temporal Biased Relocation to address these issues, reporting improved reconstruction across dynamic-scene datasets, including recovery of small, rapidly moving objects.

  • Problem

    Small, rapidly moving objects may disappear and fine textures may be poorly recovered, while increasing training or representational capacity does not necessarily resolve these failures.

    Source: §1 · p. 2

  • Method

    Seed Injection lifts observations from under-reconstructed dynamic regions into 3D, while Temporal Biased Relocation uses time-resolved optimization signals to guide relocated capacity.

    Source: §1 · p. 2

  • Results

    Experiments across multiple dynamic-scene datasets demonstrate improved reconstruction in overall scenes and highly dynamic regions, including recovery of small, rapidly moving objects.

    Source: §1 · p. 3, §5 · p. 10

  • Takeaways & Limitations

    The paper concludes that allocation must address both support for under-represented regions and the spatial and temporal destinations of relocated capacity.

    Source: §1 · pp. 2–3

    Seed Injection may miss rapidly moving objects when their appearance resembles surrounding regions, producing weak adjacent-frame differences; evaluation on more diverse, highly dynamic datasets remains valuable.

    Source: §5 · p. 10

Abstract

from arXiv · show

Dynamic Gaussian representations have made high-quality, real-time volumetric video increasingly practical, yet challenging motion can still break their reconstruction: small fast-moving objects may disappear, and fine textures in highly dynamic regions are often lost. In particular, simply training longer or using more Gaussians may still fail to recover small, fast-moving objects. We argue that the key bottleneck is not representational capacity itself, but where and when Gaussian primitives are allocated. Existing allocation strategies rely on current Gaussian locations and often overlook the spatiotemporal destinations of relocated Gaussians. To address these limitations, we introduce two complementary strategies. First, Seed Injection identifies under-reconstructed dynamic regions using temporal appearance changes and reconstruction errors, and initializes new Gaussians at geometrically plausible 3D locations through multi-view observations, independent of existing Gaussian locations. Second, Temporal Biased Relocation uses time-resolved optimization signals to guide the spatiotemporal placement of relocated Gaussians. Both strategies maintain a fixed Gaussian budget without modifying the underlying representation. Experiments across multiple dynamic-scene datasets demonstrate improved reconstruction quality, particularly in highly dynamic regions, recovering small, rapidly moving objects and fine-grained appearance details.

1 INTRODUCTION

Challenging motion can leave small objects and fine textures poorly reconstructed, and adding capacity alone may not solve the problem. The paper frames these failures as Gaussian-allocation problems and introduces complementary strategies for supplying support and choosing where and when it is placed.

1 INTRODUCTION

  • Small, rapidly moving objects may disappear and fine textures in highly dynamic regions may be poorly recovered, even when training longer or adding Gaussians does not resolve the failures.The authors therefore examine whether support reaches challenging regions and is placed at appropriate locations and timestamps.Source: §1 · p. 2
  • Seed Injection combines temporal intensity changes and reconstruction errors across synchronized multi-view images, then lifts selected observations into plausible 3D locations independently of existing Gaussians.This supplies support to under-represented dynamic regions and may recover content otherwise missing.Source: §1 · p. 2
  • Temporal Biased Relocation preserves time-resolved optimization signals to guide relocated Gaussians toward spatial and temporal destinations where reconstruction difficulty arises.It complements Seed Injection’s allocation of new support to under-represented regions.Source: §1 · p. 2
  • Applied to FreeTimeGS without changing its representation, the two strategies improve reconstruction across multiple dynamic-scene datasets, including recovery of small, rapidly moving objects.They address additional support for under-represented regions and appropriate destinations for relocated capacity, respectively.Source: §1 · p. 3

2 RELATED WORK

Prior dynamic Gaussian methods model motion through deformation or explicit spatiotemporal representations, while allocation methods often depend on existing primitives. The paper positions its approach as adding support to under-represented regions and guiding relocated capacity across space and time.

Dynamic Gaussian Splatting

  • Dynamic Gaussian methods model temporal change through deformation or explicit spatiotemporal representations, but challenging dynamic regions may remain under-represented.FreeTimeGS uses flexible temporal lifespans; the paper says these advances do not explicitly address allocation across space and time.Source: §2 · p. 3

Gaussian Densification and Relocation

  • Densification clones or splits existing Gaussians, while relocation redistributes underused capacity; prior temporal cues often guide primitive selection more than destination placement.The proposed approach adds support in under-represented dynamic regions and uses timestamp-specific signals for relocated capacity.Source: §2 · p. 3

Gaussian Allocation for Under-Reconstructed Regions

  • Prior under-reconstruction methods rely on existing primitives, external feature matching, or a coarse depth prior, whereas this approach lifts observations using synchronized multi-view geometry.The cited alternatives are Error-based densification, LPM, and SpaceTimeGS, respectively.Source: §2 · p. 3

3 METHOD

The method combines Seed Injection, which adds support to under-reconstructed dynamic regions, with Temporal Biased Relocation, which selects when and where relocated capacity is placed. Both operate within FreeTimeGS without changing its representation.

3.1 PRELIMINARIES

  • FreeTimeGS maintains a fixed Gaussian budget by recycling low-opacity primitives toward high-scoring primitives, with sampling scores based on spatial gradients and opacity.The method reviews this relocation strategy as the baseline for its temporal modification.Source: §3.1 · p. 4, §3 · p. 4

3.2 SEED INJECTION

  • Seed Injection targets pixels that are both dynamic and poorly reconstructed, then matches sampled candidates across synchronized views using viewing-ray proximity to estimate 3D seed locations.Geometrically consistent ray pairs yield seed positions at the midpoint of their closest points.Source: §3.2 · p. 5
  • Seed Injection reinitializes available low-opacity Gaussians at lifted seed positions, keeping the Gaussian budget fixed, and jointly optimizes them with the existing representation.Injection begins midway through training; relocation is temporarily disabled while frames receive 10 additional optimization iterations each.Source: §3.2 · p. 6

3.3 TEMPORAL BIASED RELOCATION

  • Temporal Biased Relocation samples a timestamp according to per-time positional-gradient magnitudes, placing the relocated Gaussian at the target trajectory’s position at that time.Gradient magnitudes are averaged across observations at each timestamp before normalization, and duration is adjusted for the shifted temporal center.Source: §3.3 · p. 6

4 EXPERIMENTS

Across datasets, the method improves reconstruction in dynamic regions, including recovering small, rapidly moving objects and preserving fine details. Ablations show benefits from both components and further gains when combined.

4.1 EXPERIMENTAL SETUP

  • The experiments cover N3DV, CMU Panoptic, and a custom dataset; the custom Baseball and Break sequences target small, rapidly moving objects.The custom evaluation reserves a spatially intermediate camera for testing and uses 1.5M Gaussians and 100K training iterations.Source: §4.1 · p. 7

4.2 COMPARISON EXPERIMENTS

  • On N3DV’s relatively mild-dynamics scenes, the method improves reconstruction over FreeTimeGS, particularly in dynamic regions, while preserving well-reconstructed static content.The six-scene evaluation uses 300 frames per scene, with camera 0 for testing and the remaining cameras for training.Source: §4.1 · p. 7, §4.2 · p. 8
  • The method recovers juggling balls and a missing baseball and pirate roulette, while better preserving hand, bat, and glove details.On CMU Panoptic, it also improves reconstruction across varied dynamic content and under-reconstructed static regions.Source: §4.2 · pp. 8–9, Figure 3 · p. 8

4.3 ABLATION STUDIES AND ANALYSIS

  • Seed Injection helps recover poorly represented fast-moving objects, while Temporal Biased Relocation improves dynamic and under-reconstructed static regions; together they further improve quality with only a small training-time increase.The ablation averages three CMU Panoptic sequences and uses 500K Gaussians and 50K training iterations.Source: §4.3 · p. 9, Table 4 · p. 9, Figure 4 · p. 9
  • Injected Gaussian centers concentrate around rapidly moving glove, bat, and baseball regions, illustrating where Seed Injection allocates capacity.The centers are projected onto the ground-truth test view at their injection timestamps.Source: §4.3 · p. 9, Figure 5 · p. 9
  • Temporal Biased Relocation generally uses fewer relocation operations than FreeTimeGS, suggesting temporal optimization signals guide Gaussians toward more effective destinations and reduce repeated relocation.Source: §4.3 · p. 10

5 CONCLUSIONS

The framework combines Seed Injection and Temporal Biased Relocation to improve reconstruction of challenging dynamic content while preserving FreeTimeGS’s representation. It improves reconstruction across multiple datasets, but Seed Injection can miss objects whose appearance resembles their surroundings.

5 CONCLUSIONS

  • Across multiple dynamic-scene datasets, the framework improves reconstruction quality, especially in highly dynamic regions, and recovers small, rapidly moving objects that disappear in the baseline.The approach combines Seed Injection with Temporal Biased Relocation while preserving FreeTimeGS’s underlying representation.Source: §5 · p. 10
  • Seed Injection may miss rapidly moving objects when their appearance resembles surrounding regions, and broader evaluation on more diverse, highly dynamic datasets remains valuable.The stated detection limitation arises when adjacent-frame differences are weak despite substantial motion.Source: §5 · p. 10
  • The authors used an LLM only for proofreading and readability edits, which they reviewed and verified.Source: AI USE STATEMENT · p. 10
  • The custom dataset was used with permission, most visible faces were blurred, and the authors report no other known ethical concerns.Source: ETHICS STATEMENT · p. 10
  • The paper describes its method and evaluation in Section 4 and supplementary materials, and states that source code will be released upon publication.Source: REPRODUCIBILITY STATEMENT · p. 10

A.1 EFFECT OF INCREASED MODEL CAPACITY AND TRAINING

Doubling FreeTimeGS’s Gaussian count and training iterations does not recover the rapidly moving ball, whereas the proposed method recovers it using the original budget and schedule.

A.1 EFFECT OF INCREASED MODEL CAPACITY AND TRAINING

  • FreeTimeGS fails to reconstruct the rapidly moving ball even when both its Gaussian count and training iterations are doubled.Source: §A.1 · p. 14
  • The proposed method recovers the ball with FreeTimeGS’s original Gaussian budget and training schedule, pointing to allocation rather than capacity or optimization time as the issue.Source: §A.1 · p. 14
  • With 3M Gaussians and 0.2M iterations, FreeTimeGS still misses the ball, while the proposed method recovers it with 1.5M Gaussians and 0.1M iterations.The comparison indicates that simply increasing capacity and training time does not resolve this reconstruction failure.Source: Figure 6 · p. 14, §A.1 · p. 14

A.2 ADDITIONAL IMPLEMENTATION DETAILS

The implementation builds dynamic seed maps from reconstruction error and temporal change, lifts sampled seeds into 3D using cross-view rays, and guides relocation timestamps with time-specific gradients.

A.2 ADDITIONAL IMPLEMENTATION DETAILS

  • The seed map multiplies a rendered-versus-ground-truth reconstruction error map by an intersected temporal-difference mask, emphasizing poorly reconstructed dynamic regions.The intersection uses changes relative to both neighboring frames, suppressing transient differences present in only one direction.Source: Figure 7 · p. 14, §A.2 · p. 15
  • Multi-view Seed Lifting matches sampled seed candidates across views by viewing-ray proximity and uses their resulting 3D locations to initialize Gaussians.The procedure filters ray pairs by positive depth and ray distance, then uses the midpoint of closest points for each retained seed.Source: §A.2 · p. 15
  • Temporal Biased Relocation selects a relocation timestamp from the target Gaussian’s timestamp-specific mean 2D positional gradient magnitudes.The relocated temporal center is set to the sampled timestamp; the spatial center is shifted using the target’s velocity and the time difference.Source: §A.2 · pp. 15–16

A.3 ADDITIONAL ABLATION STUDIES AND ANALYSIS

Ablations show that seed requirements vary by frame and content, while enough candidate seeds, at least four camera views in the tested sequence, and temporal-biased relocation support reconstruction.

A.3 ADDITIONAL ABLATION STUDIES AND ANALYSIS

  • At Nseed = 16 the target objects are generally recovered, but Nseed = 48 is used in the main experiments for more robust seed generation across frames and scenes.Requirements vary by frame: the bat becomes clearer as Nseed rises, while the baseball is recovered at all tested values in one frame and from Nseed = 8 in another.Source: §A.3 · p. 17, Figure 8 · p. 17
  • Reconstruction remains largely consistent across Nmax ∈{16, 32, 64, 256}; even Nmax = 16 recovers the baseball, glove, and bat when a sufficient candidate-seed pool is available.Source: §A.3 · pp. 17–18, Figure 9 · p. 18
  • Two camera views yield inaccurate baseball reconstruction, whereas four or more views recover it with similar quality for C = 4 and C = 8.The main experiments use C = 8 for more reliable correspondences across frames and scenes.Source: §A.3 · p. 18, Figure 10 · p. 19
  • Temporal Biased Relocation consistently uses fewer relocation operations during training than the original FTGS.The authors interpret this as indicating more effective Gaussian allocation during optimization.Source: Figure 11 · p. 19, §A.3 · p. 19

A.4 QUALITATIVE COMPARISONS

This section presents qualitative comparisons across N3DV, CMU Panoptic, and Custom dataset sequences.

A.4 QUALITATIVE COMPARISONS

  • N3DV comparisons cover the Flame Steak, Sear Steak, and Cook Spinach sequences.Figure 12 presents qualitative results on N3DV.Source: §A.4 · p. 19, Figure 12 · p. 20
  • CMU Panoptic comparisons cover the Football and Baseball sequences.Figure 13 presents qualitative results on CMU Panoptic.Source: §A.4 · p. 19, Figure 13 · p. 21
  • The Custom dataset comparison covers the Break sequence.Figure 14 presents qualitative results on the Custom dataset.Source: §A.4 · p. 19, Figure 14 · p. 21

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