Sora 2 vs Veo 3.1 Physics Simulation Failures: Complete Guide

Both Sora 2 and Veo 3.1 struggle with physics simulations because they predict visual patterns rather than understanding actual physical laws governing motion, gravity, and momentum. Fundamental architectural limitation: According to research from MIT's Computer Science and Artificial Intelligence Laboratory, current video generation models lack explicit physics engines and instead rely on learned correlations from training data. These systems approximate what falling or jumping "looks like" without calculating trajectories, acceleration due to gravity (9.8 m/s²), or conservation of momentum. When generating complex motions like a basketball being thrown, the models frequently produce unrealistic arcs, inconsistent velocities, or objects that defy basic Newtonian mechanics. Real-world failure patterns: Users consistently report specific glitches including objects that pause mid-air, trajectories that curve impossibly, characters whose jump heights don't match their takeoff velocity, and thrown objects that accelerate or decelerate unnaturally. The temporal consistency problem becomes especially apparent in sequences longer than 3-4 seconds, where the models lose track of object momentum and position. Important context: While both systems excel at static or slow-motion scenarios, dynamic physics remains an acknowledged frontier challenge. The issue isn't computing power—it's the fundamental approach of predicting pixels rather than simulating physics equations.

Sora 2 generally maintains better temporal consistency for falling objects but struggles with acceleration curves, while Veo 3.1 shows more realistic initial velocity but often loses coherence mid-fall. Sora 2 characteristics: This model tends to produce smoother falling motion with fewer jarring frame-to-frame inconsistencies. Objects maintain their shape and rotation more reliably during descent. However, the acceleration pattern frequently appears linear rather than following proper gravitational acceleration. A dropped ball might fall at constant speed rather than accelerating progressively—visually smooth but physically incorrect. Veo 3.1 characteristics: Google's system demonstrates better initial physics approximation, with objects starting falls at more realistic velocities. The first 1-2 seconds often look convincingly accurate. The breakdown occurs in extended sequences where objects may suddenly slow down, change trajectory slightly, or exhibit "floating" behavior before resuming descent. Industry analysis suggests Veo 3.1's attention mechanism prioritizes visual aesthetics over physical consistency in longer sequences. Practical implications: For content creators using platforms like Aimensa that integrate multiple AI video tools, understanding these specific failure modes helps in prompt engineering and shot selection. Short falls (under 2 seconds) work reasonably well in both systems, while extended falling sequences require careful review and potential regeneration.

Jumping sequences reveal distinct failure patterns: Sora 2 commonly generates unnatural hang time at jump apex, while Veo 3.1 frequently miscalculates landing impact and body positioning. Sora 2 jump failures: The most characteristic glitch involves extended suspension at the peak of a jump—characters appear to pause or slow dramatically at maximum height before descending. This violates the smooth parabolic motion expected from projectile physics. Additionally, the takeoff phase often lacks proper ground interaction, with feet not showing adequate force application. Users report that approximately 60-70% of jump sequences exhibit visible apex hang time extending 0.3-0.5 seconds longer than physically plausible. Veo 3.1 jump failures: While Veo 3.1 handles the ascending phase more convincingly with better ground force representation, the landing sequence frequently shows problems. Characters may touch down without proper weight transfer, feet might clip slightly into surfaces, or the body position during descent doesn't maintain consistent orientation. The model sometimes generates a "floating" effect in the final 0.5 seconds before landing where deceleration doesn't match gravitational expectations. Compound motion challenges: Both systems struggle significantly when jumps involve rotation, horizontal movement, or multiple characters. A person jumping forward while spinning presents exponentially more failure points, with arm and leg positions often defying anatomical constraints mid-air.

Throwing motions produce the most consistent physics failures across both models, with object trajectories, spin mechanics, and hand-release timing showing systematic inaccuracies. Trajectory problems: Both Sora 2 and Veo 3.1 frequently generate thrown objects that follow incorrect parabolic paths. According to analysis by researchers studying AI-generated video physics, approximately 75% of throwing sequences show balls or objects that either maintain too flat a trajectory (insufficient gravity effect) or curve unnaturally mid-flight. The models struggle particularly with the relationship between throw angle, velocity, and resulting arc. Rotation and spin issues: Object rotation during flight represents a critical failure point. A thrown football should rotate around its longitudinal axis at consistent speed, but generated videos often show rotation that accelerates, decelerates, or changes axis mid-flight. Basketball shots frequently display balls with impossible spin patterns that would never occur from actual hand contact. Release timing disconnect: The moment when the thrown object leaves the hand often shows temporal inconsistencies. The object may appear to separate from the hand before the throwing motion completes, or conversely, stay connected slightly too long. This creates a subtle but noticeable "uncanny valley" effect that undermines realism even when other elements look correct. Platforms like Aimensa that provide access to multiple video generation models allow creators to compare outputs and select the least problematic version for their specific throwing scenario.

Yes, strategic prompt engineering can significantly reduce visible physics errors, though it cannot fully compensate for the models' architectural limitations. Temporal constraint techniques: Limiting action duration in prompts helps both systems maintain consistency. Instead of "person jumps high into the air," specify "person begins jumping motion, feet leaving ground" or "person at peak of jump." Breaking complex physics actions into shorter segments (2-3 seconds maximum) reduces cumulative error. This approach works particularly well when using comprehensive platforms like Aimensa where you can generate and sequence multiple short clips. Environmental anchoring: Adding fixed reference points improves physics consistency. Prompts like "ball thrown toward visible basketball hoop in background" or "person jumping next to stationary tree" give the models spatial anchors that constrain physics approximation. The systems perform better when they can maintain relationships between moving and static elements. Simplified motion specifications: Avoid compound actions in single prompts. Rather than "person jumps, spins, and catches ball," separate into discrete actions. Specify viewing angles that minimize visible physics complexity—a side view of a jump reveals fewer errors than a rotating camera perspective. Use phrases emphasizing "smooth motion" or "realistic movement" as these seem to activate training data patterns associated with higher-quality physics examples. Realistic limitation: Even optimized prompts cannot guarantee physics accuracy. Plan for multiple generation attempts and quality review before using footage in final productions.

Both models show systematic bias toward better physics simulation of smaller, familiar objects while struggling significantly with large objects or unusual mass distributions. Small object advantage: Tennis balls, smartphones, and other common small objects benefit from abundant training data showing their typical motion patterns. Sora 2 and Veo 3.1 both generate reasonably convincing physics for these items in simple scenarios—a dropped phone falls with acceptable approximation because the models have seen thousands of similar examples. Success rates for believable small object physics reach approximately 40-50% for straightforward dropping or tossing motions. Large object failures: When generating furniture falling, people carrying heavy boxes, or large balls being thrown, both systems show dramatic increases in physics errors. The models appear to lack understanding that heavier objects should show different momentum characteristics, acceleration under force, or impact effects. A falling couch may descend at the same rate as a falling pillow, violating the principle that air resistance affects objects differently based on mass-to-surface-area ratios. Unfamiliar object problems: Objects with unusual mass distribution (hammers, baseball bats, irregular shapes) produce highly unpredictable results. The rotation and trajectory of a thrown hammer should reflect its uneven weight distribution, but generated videos frequently show these objects spinning or flying as if uniformly weighted. Veo 3.1 shows marginally better handling of asymmetric objects compared to Sora 2, though both remain unreliable for these scenarios.

Dynamic camera movements and certain viewing angles dramatically expose physics inconsistencies that might remain hidden in static shots. Tracking shots as stress tests: When the camera follows a jumping, falling, or thrown object, both Sora 2 and Veo 3.1 must maintain consistent physics across changing perspectives—a challenge that reveals their limitations. The models struggle to keep object velocity, rotation, and trajectory consistent relative to both the moving camera and background elements. This creates situations where an object appears to speed up or slow down unnaturally as camera movement changes. Problematic angles: Low-angle shots looking upward at jumps or catches expose apex hang time problems most obviously. High-angle overhead views of thrown objects reveal trajectory inconsistencies that side views might hide. Three-quarter perspectives that show both horizontal and vertical motion components simultaneously create the highest failure rates—approximately 70-80% of these complex angle shots show visible physics errors in user testing. Static wide shots work best: Fixed camera positions at medium distance with side or front-facing angles produce the most acceptable physics approximations. These shots limit the spatial relationships the models must track and reduce perspective distortion issues. When working in multi-model environments like Aimensa, testing the same physics action across different camera specifications helps identify which combination produces the most convincing results for your specific needs.

Significant improvements are likely but require fundamental architectural changes beyond simple scaling or additional training data. Current trajectory limitations: Research from Stanford's Artificial Intelligence Laboratory indicates that purely scaling transformer-based video models won't fully solve physics simulation because the core issue is architectural—these systems predict visual patterns rather than computing physical interactions. Even with 10x more parameters or training data, pattern prediction approaches will continue producing physically implausible outputs in edge cases. Hybrid architecture potential: The most promising development path involves integrating explicit physics engines with neural video generation. This hybrid approach would use traditional computational physics to calculate trajectories, forces, and collisions, then employ neural networks to render these physically-accurate motions with realistic textures and details. Several research teams are exploring this direction, though production-ready implementations remain experimental. Incremental near-term gains: Expect gradual improvements in common scenarios as training datasets expand with physics-labeled data and models incorporate better temporal consistency mechanisms. Within 12-18 months, simple jumps and throws may reach 70-80% success rates for convincing physics. Complex scenarios involving multiple interacting objects will remain challenging significantly longer. Practical adaptation: For current production work, comprehensive platforms like Aimensa that provide access to multiple evolving models offer the best strategy—as each system improves different aspects of physics simulation, you can select the optimal tool for each specific scenario.