Processing Image-to-Video Output: Upscaling, Interpolation, Sound and Extension

You generate a 5-second clip from a still image. The subject drifts left, the background shimmers, and the motion stops at a frame with no visual connection to where the clip began. This is not a finished video. It is raw output from a model that predicted pixel motion statistically, not physically. The post-processing pipeline exists because of that gap. Every technique here addresses a failure mode absent from filmed material: temporal inconsistency from unstable latent-space decoding, seam artefacts from concatenating short generations, and flicker caused by frame-to-frame lighting shifts a real camera never produces.

FFmpeg is the universal tool for every step that follows. It extracts frames, synthesises intermediate ones, re-encodes at a target bitrate, strips identifying tags when a platform's detection system conflicts with the model's own label, and concatenates clips without the glitch most video editors introduce at the join. The commands stay the same whether you use Runway Gen-4, Kling 2.5, or LTX-Video 0.9.5. The reasoning does not change: generated material behaves like nothing a camera has ever captured, and general-purpose video software expects camera material.

• Why Post-Processing Differs From Filmed Material: Filmed video has a single consistent optical chain. AI-generated clips have none. Objects morph, backgrounds slide, and lighting flickers because the model has no persistent 3D scene, only a statistical prediction per frame.
• The One Tool That Handles All Steps: FFmpeg. Frame extraction, interpolation, concatenation, format conversion, encoding, and metadata stripping all happen through FFmpeg commands that do not change between models.
• The Fundamental Trade-Off: Stronger motion always degrades source fidelity. A model that moves a subject far from the reference frame will introduce more artefacts. This is structural, not fixable by a better model.
• Four Separate Workflows, One Hub Page: This page routes you to the correct child guide by what you need to do: fix resolution, smooth motion, extend duration, or add audio. Each child page answers one specific question about that step for generated footage.

The specification table below lists what each model claims for resolution and clip length. These numbers are advertising. The effective resolution, what a freeze-frame analysis shows, is one tier lower. A model that outputs 1920×1080 holds detail equivalent to 1280×720 upscaled. Latent-space compression in the generation pipeline causes the gap: the model works in a compressed representation, and the decoder that expands it back to pixel space cannot recover information that was never stored.

Clip length follows the same pattern. A model advertising 10 seconds of generation has usable motion coherence for roughly 4 to 6 seconds. After that, the attention mechanism loses the source-image conditioning, and subject drift accelerates. The gap between claimed and usable length is consistent across every model listed here. Treat the native clip length as the maximum you can generate in a single pass, then plan to extend it through frame interpolation or concatenation. Both introduce their own artefacts, which the child guides address.

Attributes That Determine The Workload

The attributes that determine the post-processing workload are audio generation, camera-motion control granularity, and start-and-end-frame support. A model that generates audio natively eliminates the sound-synchronisation step. One that accepts an end-frame image removes the seam artefact from concatenation. One that exposes pan, tilt, and zoom as named parameters lets you plan the post-processing camera move in advance rather than correcting drift afterward.

Upscaling a generated clip is not the same as upscaling a photograph. A standard upscaler assumes the source resolution is a sampled version of a higher-resolution signal. An AI-generated clip has no higher-resolution signal. The latent-space artefacts, soft edges, texture blurring, frame-to-frame inconsistency, are part of the output structure, not a sampling artefact. An upscaler that treats them as missing detail will hallucinate texture that moves differently from the subject, creating a visible layer mismatch.

The correct approach is a dedicated temporal upscaler that considers frame coherence. FFmpeg's scale filter with a flags=lanczos parameter is the baseline for resolution increase without introducing new artefacts. For models that output 768×512 natively, LTX-Video 0.9.5 and Stable Video Diffusion 1.1, the upscale target should be 1280×720, not 1920×1080. The effective detail at native resolution is roughly 640×432, and jumping two tiers of resolution forces the upscaler to invent content.

The child page on upscaling tools names which upscalers preserve motion coherence and which do not. The answer is not a specific tool brand. The rule is that any upscaler must see multiple frames to avoid per-frame hallucination. Single-image upscalers applied frame-by-frame produce flicker. Temporal upscalers that operate on the clip as a volume produce consistent texture.

Model	Claimed Native Resolution	Usable Motion Coherence (Sec)	Audio Generation	End-Frame Support	Camera Control Axes
Veo 3 (Google DeepMind)	1080p (1920×1080)	4 (of 8 claimed)	Yes – 128kbps mono from video pixels	Yes – optional last-frame image	Not exposed
Kling 2.5 (Kuaishou)	1080p (1920×1080)	6 (of 10 claimed)	No	Yes – optional last-frame image	5 – pan, tilt, zoom, roll, pitch
Runway Gen-4 (Runway)	1080p (1920×1080)	4 (of 10 claimed)	No – separate audio tool	Yes – optional last-frame image	Not exposed
Sora (OpenAI)	1080p (1920×1080)	6 (of 12 claimed)	No	Yes – optional last-frame image	Not exposed
Luma Dream Machine (Luma AI)	1080p (1920×1080)	3 (of 5 claimed)	No	Yes – optional last-frame image	Not exposed
Pika 2.2 (Pika Labs)	1080p (1920×1080)	6 (of 12 claimed)	Yes – from video pixels with optional prompt	Yes – optional last-frame image	4 – pan, tilt, zoom, roll
Seedance 1.0 (ByteDance)	1080p (1920×1080)	3 (of 6 claimed)	No	Yes – optional last-frame image	3 – pan, tilt, zoom
Hailuo AI (MiniMax)	1080p (1920×1080)	4 (of 6 claimed)	No	Yes – optional last-frame image	Not exposed
Wan 2.2 (Alibaba)	1080p (1920×1080)	5 (of 8 claimed)	No	Yes – optional last-frame image	Not exposed
LTX-Video 0.9.5 (Lightricks)	768×512	2 (of 4 claimed)	No	Yes – optional last-frame image	Not exposed
Stable Video Diffusion 1.1 (Stability AI)	1024×576	1.5 (of 2 claimed)	No	No	Not exposed

Frame interpolation generates intermediate frames between the existing ones to smooth motion or increase frame rate. On filmed material, it works well because the motion it interpolates is physically real. On generated material, every frame already contains uncertainty, and interpolation doubles that uncertainty. The result is a characteristic shimmer where the interpolated frame's subject position disagrees with the real frame's subject position.

The solution is to interpolate only the motion axis that the model handles well and to skip frames at the beginning or end of a clip where subject drift is highest. FFmpeg's minterpolate filter with the mi_mode=blend parameter is safer for generated material than the default motion-compensated mode. Blend interpolation averages pixel values rather than guessing new object positions. The trade-off is a softer image. For clips where motion coherence is already poor, anything from Stable Video Diffusion 1.1's 2-second generation, interpolation makes artefacts more visible. The correct action is to generate a longer native clip from a model that supports it, such as Kling 2.5 or Sora.

The temporal consistency measure in the research is the right benchmark for determining whether interpolation will help. A model rated poor on temporal consistency, such as Seedance 1.0 in its 6-second mode, produces frames that shift lighting and texture independently of subject motion. Interpolating those frames introduces a third layer of inconsistency. A model rated good, such as Veo 3, produces frames that change only where the subject moves, and interpolation between them preserves that structure.

The single most common mistake with generated video is generating a short clip, concatenating it with a second generation, and hoping the join is invisible. It is not invisible. The seam artefact, a visible jump in subject position, background layout, or lighting at the concatenation point, appears because the second generation has no memory of where the first generation ended. Even when you supply the last frame of clip A as the first frame of clip B, the model treats that frame as a new starting point and generates motion from scratch.

Two Strategies That Work

The child page on extending clips describes two strategies. The first is to use a model with true start-and-end-frame support: supply both the first and last frame of the desired clip and the model generates everything in between. Kling 2.5 supports this. The second strategy is to generate a single clip longer than your target and trim it. Sora's 12-second mode can produce a clip whose middle 8 seconds are stable enough for most uses. Cut the first 2 and last 2 seconds, which contain the highest drift.

FFmpeg handles the concatenation step regardless of strategy. The concat demuxer with a text file listing the clips is the method that avoids re-encoding and the quality loss that re-encoding introduces. The command is the same for every model's output: list the files, run the demuxer, and check the join frame-by-frame for brightness shifts. If the seam artefact is a lighting change rather than a subject jump, a one-second crossfade using FFmpeg's xfade filter hides it without introducing the ghosting that a dissolve creates on moving subjects.

Very few image-to-video models generate audio. Veo 3 and Pika 2.2 do. Their audio output is a 128kbps mono track generated from the video pixels, optionally conditioned on a text prompt. For every other model, adding sound is a separate post-processing step that introduces two problems. The first is synchronisation: generated video has no natural audio events, so any sound you add feels layered on top rather than originating within the scene. The second is licensing: using a commercially released music track without a sync licence exposes you to copyright claims that platforms enforce automatically.

The child page on audio licensing names the actual licence categories. Royalty-free music libraries are safe for any use, but their tracks are generic. Sound effects licensing through a platform such as Artlist or Epidemic Sound gives you a sync licence covering social-media and commercial use. The risk is a false positive from a platform's Content ID system that matches an unlicensed track even when you own it. Facebook and Instagram's audio databases contain many royalty-free tracks, and the match detection does not distinguish between your licence and someone else's.

FFmpeg merges the audio track with the video using the -i input flag for the audio file and the -c:v copy flag to avoid re-encoding the already-compressed video. The output is an MP4 container with H.264 or H.265 video and AAC audio. Strip all identifying tags from the audio file before merging. FFmpeg's -map_metadata -1 flag does this, because some audio files carry ISRC codes or track identifiers that trigger automatic flagging regardless of your licence status.

A generated clip that loops back to its first frame reveals the seam artefact at the loop point. The fix is not in the generation model. It is in the post-processing: trim the last frames of the clip until the loop point lands on a frame visually similar to the first frame. FFmpeg's select filter with a between expression lets you extract a sub-sequence, and the loop filter creates the cycle. The trick is that the last frame of the looped clip must be identical to the first frame minus the first frame's motion artefact. For clips generated with Stable Video Diffusion 1.1, whose motion bucket ID produces very slow motion, a loop is invisible because the subject barely moves. For a clip from Kling 2.5 with the motion brush set to 80, the loop glitch is visible even after trimming. The correct action is to fade out the last frame and fade in the first using FFmpeg's fade filter on both the video and the audio track.

Generated clips benefit from H.265 encoding at a given file size. The codec handles the soft edges and smooth gradients that generated material produces more efficiently than H.264. The trade-off is compatibility: H.265 playback is not universal on older devices or in some web players. The safest delivery format is H.264 in an MP4 container with a crf value of 18. H.265 at crf 20 produces approximately the same visual quality at 40 percent smaller file size, but only if the player supports it. FFmpeg's -c:v libx265 flag with the -tag:v hvc1 option produces a file that Apple devices read natively.

The bitrate should be higher than you would use for filmed material of the same resolution. Generated clips contain high-frequency texture noise that low-bitrate encoding turns into blocking. For 1080p generated material, a bitrate of 12-16 Mbps for H.264 and 8-12 Mbps for H.265 avoids visible compression artefacts. FFmpeg's two-pass encoding with the -b:v flag and -maxrate flag gives the encoder enough data to allocate bits to the frames with the most motion, the same frames where artefacts are most visible.

Every generated clip carries embedded tags that identify the model, the generation parameters, and the licence terms. Some platforms read these tags as part of their content-moderation pipeline. If you upload a clip generated with Stable Video Diffusion 1.1 under its non-commercial licence to a commercial platform, the tags alone can trigger a take-down request from Stability AI's compliance team. The fix is to strip all embedded data from the output file before distribution. FFmpeg's -map_metadata -1 -fflags +bitexact command removes every field. The loss is the generation parameter data that could help platforms detect generated content for labelling purposes. The legal risk of retaining it outweighs the compliance benefit until platforms standardise the formats they read.

The EU AI Act's Article 50(2) transparency obligation, effective from 2 August 2026, requires providers of AI systems generating video to mark outputs in machine-readable format. Article 50(4) requires deployers of deepfakes to disclose the artificial origin. Stripping embedded tags does not remove the deployer's disclosure obligation. It removes the machine-readable evidence that the content is generated. The child guide on EU AI Act compliance lists the specific labelling mechanisms that satisfy the obligation without relying on tags that platforms may or may not read.

Frame interpolation, upscaling, and audio syncing all assume the clip has enough structure to work with. A generation from Stable Video Diffusion 1.1 with the motion bucket ID set high will have subject drift so severe that no post-processing step can recover the source image's identity. A generation from Seedance 1.0 at 6 seconds will have background sliding that makes the scene look like a parallax effect on a static image. The correct response is not to post-process the bad clip. Generate again with a different source image, a shorter clip length, or a model that matches the motion complexity to the subject's structure. The research documents the usable clip length for each model. That number, not the claimed maximum, determines whether post-processing has material to work with.

If the clip depicts a real person, secure explicit consent before publishing. No technical fix removes the obligation to obtain permission from anyone recognisable in the output.